Download The hard part of taking advantage of this flood of geospatial

A concept model for knowledge production GIS
This explains how the definition of a knowledge development GIS (KDGIS) works
as a model. This piece is the set-up for a discussion of the elements of the model and
their relation to systems and practice that will be followed by the critique of DIKW
models you saw earlier. Being explicit with the KDGIS definition and model will make
that critique more simple and specific.
FINISH THE PHILOSOPHY OF KNOWLEDGE
CONNECT THIS WITH THE CONTENT FROM THE PHILOSOPHY OF
KNOWLEDGE.
REVISIT BENYON AND THE PROPERTIES OF MODELS
SKETCH THE STRUCTURED FUNCTIONS
There is no conceptual model for GIS as systems and not representations of
system elements or applications. Concept models for GIScience have been proposed and
applied as the discipline has developed and are relevant to system definitions,
cartography, analysis and application. These apply to isolated or incremental system
activity but they do not treat the composition and function of GIS as an integrated system.
In addition they are not sensitive to systems’ extraorganizational situatedness. Need for a
model that integrates these is especially apparent for GIS with frequent system state
change. Knowledge development GIS (KDGIS) commonly experience system state
change due to turnover in personnel or worldviews, short term funding agreements, and
exposure to paradigm or priority shifts.
A KDGIS model will support GIS practitioners as they engage with GIS situated
in ecologies where problems arise, methodologies are developed, outcomes are
disseminated, and uses are defined. This model will represent the structure, operation
and constraints in these complex situations for the purpose of communication, analysis,
and explanation (Kangassalo 1983). It will constrain and focus discourse on
implementation by limiting the range of concepts that can be expressed (Kangassalo
1983; Benyon 1997). Systems’ operational levels are the KDGIS principal concern that
addresses information flows and the process of knowledge construction and
representation (Benyon and Imaz 2009). This follows Codd’s (Codd 1970; Codd 1982)
development of the relational data model in information systems development. The
organizational and workplace components of a KDGIS can be understood in their
relationships to the structure and function at the operational level.
David Benyon (Benyon 2002) argues there is no universally agreed conception or
theory in Human-Computer Interaction (HCI) and presents a comparison of competing
methods. He argues that there are varying degrees of utility of abstractions and models
offered by the different views. The discussion here considers elements of these views
and describes how they are formative to understanding, communicating, testing or
predicting aspects of the KDGIS model. Some principles of models discussed are
aggregation, classification, structure, function, abstraction, concepts, and physical form.
The purpose of models is to suppress unnecessary detail and two ways this can be
done is with aggregation and classification. Aggregation groups related things together
represented as a single object while classification groups objects as a class. These are
forms of abstraction that reduce detail. Benyon (Benyon 2002) calls attention to the
importance of social or world views with examples of Wittgenstein’s (Wittgenstein 1953)
consideration of ‘games’ as a class and Lakoff’s (Lakoff 1987) discussion of cultural
differences in classifications. The geographic information abstractions used to structure
database models of worldviews are classification, generalization, aggregation and
association (Nyerges 1991). These work to suppress detail in data and database
development and are an important element contributing to individual systems. In a
systems model these choices are further generalized and represented as contributions to
articulation and development of information needs, worldview abstraction, schema,
database model, measurement and analysis. Emphasis on aggregation at this granularity
takes place during comparison of systems before they are unpacked to consider specific
details or differences. Aggregation of system processes is part of the ‘levels of
abstraction’ principle introduced later.
Structure and function are closely related where they are concerned with
components of the system and how their interaction is altered with system change. A
structural view places emphasis on the main entities or artifacts in the system and how
they interact. This is extended to the operations interactions between specific sets of
entities. A functional view is concerned with the movement and interactions of a
substance as it moves through the system. Structure and function come together during
observation of system dynamics as it moves from one state to another.
These aspects of structure and function tempered by abstraction and classification
play a part in GIS and are of particular concern for the KDGIS model. System structure
in GIS can be coarsely grouped into constitutive sociotechnical entities from
development of information needs through to contributions of results as evidence and
knowledge. In addition to the examples of aggregation, main entities include
measurement, integration, analysis, and production. These are part of the KDGIS model
and interactions between entities are characteristics of specific systems. Of greater
importance in KDGIS is flow through the system and ways that activity in creation and
integration of data resources as they contribute to the system as a whole. This is echoed
in system dynamics that are shaped by intra and extra organizational forces.
‘Levels of abstraction’ support structure and function where one may be part of
the other. A function might be well suited as a main entity in a structural view and visa
versa. This clarifies relationships in KDGIS where the model takes physical form as a
structure of functions. The schema (Nyerges 1989) function is made up of main entities
and interactions that comprise organizational practice and negotiation with the abstraction
(Nyerges 1991) and database model functions. Another level of abstraction can be seen
where the schema function becomes a main element part of a structural viewpoint.
Concept is part of models and is the relationship between the object of a model
and the concepts from which it is constructed. The affordances of a model rely directly
on the choice of main elements and relationships and their effective treatment of the
model’s functional needs. This pertains to alignment between the model purpose and
appropriateness of the model’s components to achieve that purpose in a suitable and
useful manner consistent with the model’s concept. Concept represented in the KDGIS
model rests on the arrangement of relationships between the model’s structures of
functions and the model as a whole. There must be consistency in ways these
relationships are arranged to approach all instances of KDGIS in a valid manner. They
must present a suitable framework to support understanding, communicating, testing or
predicting aspects of KDGIS.
From email to Tim:
Categories in the diagram:
The diagram is an information flow model through as series of sociotechnical
functions. Information needs initiate the flow and it generally moves 'up'
through the successive functions toward 'knowledge'. This is arranged after an
idealized or commonly recognized associations between functions that move
information and activity through the model. While the model has physical form
in the diagram it isn't literal, some have feedback and others may appear out of
order; 'transformation' and 'integration' are two of these. There is a cyclical
pattern where knowledge or wisdom connect back to information needs.
Emmbeddedness:
Embedded in each of the model's functions are the social and technical
interactions or iterations required for the system to be successful.
The disembeddness of the model is a deliberate reaction to the numerous models
that are not sufficiently specific about how their elements or functions are
connected. To collapse this model to an embedded state might be:
real world-> measurement-> analysis-> cartography/report-> user-> real world.
Throw in a little feedback and this is essentially the cartographic process. My
model is meant to expand the cartographic process to account for the
interactions present in situated systems.
Experience:
The position of experience between 'product' and 'knowledge' is not exclusive
to the model's process. It has an embedded quality of accumulation from within
the model and drawing from outside. Experience is operable when the model
iterates or parallel processes which accumulate at 'experience'. After that...
"The only source of knowledge is experience." Albert Einstein
Development:
Development is meant to be read as the information flow among the model's
structural functions. "Knowledge development" and "Knowledge generation" are
common in the GIScience and business lit. It is more common that knowledge
development pertains to individuals' learning activities, but is also associated
with systems applications. 'Generation' is covered in Ch. 3 of Davenport and
Prusak's 'Working Knowledge'.
One of the diagrams I collected depicts knowledge development as a cycle:
http://students.washington.edu/ewmartin/KDGIS/image011.jpg. It comes from
Nonaka, I. and H. Takeuchi (1995). The knowledge-creating company : how
Japanese
companies create the dynamics of innovation.
My moves with a definition and model for KDGIS is deliberate to make my work
easier and easier to understand. I am proposing the ideas not as final products
but as flexible initiatives that suit my current needs. When applied the model
needs to orient and frame the approach and investigation of a GIS and then 'map'
its relationships with others.
http://students.washington.edu/ewmartin/KDGIS/transfer2.jpg
The piece you just saw is the very beginning of my treatment of the many states
data take within and between organizations. In earlier conversations I used
'data stages' to describe the collection of element/functions represented in the
model. Trying to reconcile these, their interactions, and knowledge development
forced me to organize them as a model.
It has always been my intent to describe each of the model's elements in a
manner suitable to grasp the nature of their interaction. The model gives me
structure to organize and apply these thoughts.
Connecting KDGIS with 'Data Worlds' is easy.... they are the 'atomic element'
and subjects of my work. Individually they represent worlds and have
granularity when connected through practice, mandate and hierarchies of power.
Stages
This needs a better definition of the complex nature of Mode 2 knowledge development
organizations – consult Perrow and the origins of Mode 2.
Punch line is to set up the manner of interaction between the KDGIS model’s function
elements.
Data demand for scientific endeavors arise from the questions that researchers propose
(Hesse, Sproull et al. 1993; Michner and Brunt 2000). Uncertainty in social setting of
KDGIS is another source of uncertainty that can bring abrupt changes in priorities.
All are science related practices that contribute to uncertainty
Cooperation with other organizations
Standards ISO 2000
Compatibility with other works
Need to account for other works
How much of the science driven issues cross over into other flavors of KDGIS like R&D,
legal confrontation, resource management, response to government mandates, protection
of rights, or debunking the opposition? Note: all of these are volatile – wildlife is volatile
and vulnerable to change. Urban growth and planning, global warming, natural disaster
management – 2004 tsunami in Sri Lanka, economic collapse/growth. Response to the
opposition. Development of new practices (R&D). Competition. Fast moving
confrontation – epidemics and war. Resource scarcity
SITUATEDNESS
Situatedness: the occasioned properties of interactional sequences
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the contingence of action on a complex world … [is not] an extraneous problem
… but ... an essential resource that makes knowledge possible and gives action its
sense.
the coherence of action is not adequately explained by either preconceived
cognitive schema or institutionalised social norms.
Rather the organisation of situated action is an emergent property of moment-bymoment interactions
“the coherence of action is not adequately explained by either preconceived
cognitive schema or institutionalized social norms”.
The organization of situated action is an emergent property of moment-bymoment interactions between actors and the environment that they are interacting
with.
A situation is a user’s perception of a specific moment based on his/her evaluation
of the interaction with an information retrieval system and his/her plan.
Context, the representational view
• context is a form of information
• context is stable
• context is uniform
• context is delineable
• context is separate from “content”
“Ongoing production of new forms of working practice”
mutually influential interactions through engagement with internal and external forces
interactions that shape the system
goes beyond system design and organizational integration to addresses the situatedness of
GIS design and implementation that goes beyond technical practicalities to include
organizations using the system and
as these unfold in specific situations. Too often attention to system design retains focus
on system design without appropriate consideration for the
USER NEEDS
Look at Huxhold ch 16 in the MPLIS: The Guidebook
Multi-purpose geographic database guidelines for local governments
Erik De Man (De Man 1988)
P324
Much attention has been placed on the technical concerns involved in design and
implementation of GIS. There needs to be more attention paid to “…the environment of
an envisioned GIS”. This entails examination of the localized function of the system and
the “wider object system.”
P325
Sequence of functions graphic is very similar to Nyerges project flow.
See De Man 1984 – Conceptual framework and guidelines for establishing geographic
information systems.
“The value of an information system arises out of the usefulness of its resultant
information products. Information is an answer to a question. These questions emerge in
the context of a problem-solving and often in connection with managerial activities and
functions.”
P326
Information accompanies matters of importance combined with uncertainty.
P332
GENERAL OBSERVATIONS: Need recognition and system planning
P333
PLANNING: DECISION AND CHOICES: Understanding context of mission, users,
needs and questions of why and what followed by how. Requires “development of sound,
comprehensive and creative concepts rather than short-sighted mastery of techniques and
tricks.”
PROBLEM FINDING SOLVING (DIAGNOSIS) OR SOLUTION FINDING: Dutton
and Kramer (Dutton and Kramer 1978). Simon (Simon 1960) distinguishes between
different techniques that are used for handling the so-called programmed and nonprogrammed aspects of decisionmaking. Programmed: repetitive, routine so definite
procedures can be established to resolve them. Non-programmed: novel, unstructured,
‘one shot’.
P334
METHODS OF DESIGN: top-down or feedback iterative (pilot project)
UNCERTAINTIES: A dynamic and unstable environment will influence the system just
as the system may effect the uncertainty and contribute stability.
STRATEGIC CHOICES: Interconnected decision areas where choices must be made
and several areas may be connected with varying effect.
.
After this he returns to the systems approach by with specific attention to the analytical
requirements then addressing planning steps that recognize the importance of org, tech,
and design interaction.
He does not get into operation and real-time choices in operation and productivity.
Tim and Piotr
P21 Explanatory relationships rather than just description.
Testing to find if these enhance our understanding about the world.
Human-Computer-Human interactions.
How do people make use of GIS in a problem context?
Group decision making as human-computer-human interaction.
WHAT IF THIS IS REINTERPRETED AS HUMAN-COMPUTER-ACTION?
1. “Convening structures” – do these apply to org-org interaction
a. Power and control – These are between org and can be applied to an org in
the sense that certain collective conscious coalesces orgs from inside,
outside or both.
b. Subject domain – this is definitely the internal shared interest, but can also
bring orgs together.
c. Task, purpose, complexity – this aligns with ‘matters of importance
together with uncertainty (de Man 1990):326. This can also be equated
with ‘Messy GIS” where many stakeholders or interested parties require or
seek or order to focus collective action to achieve a future state.
d. P28
i. A requirement is knowledge about the natural environment – I
interpret this as interpretations of reality and certainty in cause
effect. There’s a difference between stakeholder interpretations
and values. This poses a science vs. politics standoff. The science
solution requires buy-into the same interpretations of reality. They
explain this further This can fuel KPGIS
e. Convenor of participants – The PSP as an example of multi-org situation.
Champions, leaders, board of directors and prophets at the org. level.
Discourse.
f. Choice of participants – Being hired into an org or contracted to provide a
service in the case of individual organizations. Multiorg might be criteria
to participate or favoritism. Choice for opposition and conflict.
g. Rules and norms as social structures among participants – Who is
‘allowed’ to engage with whom in a manner that maintains legitimacy and
respect.
FOLLOWING THE BUILD-UP ON KNOWLEDGE
AND RECOGNITION OF OTHER MODELS IN GIS
THE POINT ON THE OTHER MODELS IS THEY ARE PROCEDURAL AND DO
NOT ADDRESS THE SITUATEDNESS OF THE GIS OR THE ORGANIZATION
YET, THEY DO TRY TO ADDRESS HIERARCHY AND DEVELOPMENT OF
KNOWLEDGE/WISDOM.
REMEMBER TO RETURN TO THE DIKW MODEL FOR A DEFINITION OF
WISDOM
The KPGIS model is composed of interrelated functions that represent the behavioral and
organizational operations of GIS used for problem solving and decision making. It
adheres to the facts that GIS are embedded in social and cultural goals (Chrisman 1987)
and GIS need to be designed relative to their actual use (de Man 1990). KPGIS are
situated activity (Suchman 1987; Suchman 1995) made of moment-by-moment
interactions between actors and their environment. In these ways, the model is an
approach that considers GIS as a localized enterprise. For this reason the model is not
hierarchical because it represents a complex system with loose couplings (Perrow 1999).
The hierarchical and process steps of GIS design and implementation are flattened in
KPGIS. Despite this, adjacency of functions in the graphic model and sequential order of
the model’s description represents common associations experienced in practice and not
exclusivity. See graphic ??
The focus of the model is on the in-situ and lived-in use:
Descriptions of these are here:
The attention to systems’ details and organizational preparedness ignores expected livedin use or current and future development of practices, especially ones brought on by
interaction with GIS (Martin 2000). These sides of organizations or enterprises are part
of situated learning (Lave 1993; Wenger 1998), articulation work (Suchman 1995) and
computerization movements (Kling and Iacono 1995) and middle spaces (Fauconnier and
Turner 1994).
Lave and Wenger (Lave 1993; Wenger 1998) developed the community of practice
concept where practice is an outcome of situated learning as it normally occurs in an
activity, context and culture, i.e. it is situated.. Members of a community of practice
develop mastery of skill, know-how and means through mentoring relationships.
Engagement at this level is outside the realm of management and is often an
indispensable contribution to successful enterprises. In knowledge production GIS
environments this can be appreciated as a form of irregularly distributed knowledge that
is carried out and transmitted by engagement with work and exchange among peers. It
extends within and across the diverse communities of practice that perform disparate
functions in an organization and extended communities of practice represented by
discipline, education and lives outside the work environment.
Suchman (Suchman 1987) considers situation and situatedness to be the relationships
between design, function and momentary insitu engagement. Design and planning are
situated when characteristics local to implementation are incorporated in the process.
Situatedness is the position of work activity in a situation where performance involves
real time decision making to resolve problems and decisions as they arise. This is akin to
situated learning in that ‘articulation work’ (Suchman 1995) is positioned in the hands of
people doing mundane work on which the function of technical systems’ depend.
‘Computerized movements’ (Kling and Iacono 1995) are collective social activities
mobilized to adapt and adopt situated computing systems. Like other types of social
movements, computerized movements are development of ideologies of how people
arrange and order interaction with their systems. These ideologies can become inflated as
they build momentum to recruit allies that will realize the system and fashion a culture of
expectation among those who will be part of the system. Kling and Iacono’s observation
of computerized movements agrees with Campbell (Campbell 1991) and Campbell and
Masser’s (Campbell and Masser 1995) exploration of the organizational dynamics that
surround GIS implementation. Also relevant are arguments about the reality of GIS
champions’ influence on organizations’ successful adoption of and implementation of
GIS (Azad 1993; Onsrud and Pinto 1993; Budic 1994; Huxhold and Levinsohn 1995).
That social and cultural influences are an inherent part of GIS overlooks the active
computerization movements that take place in the process of rallying people and
resources for successful implementation or change.
Bringing cognitive processes or mental spaces together takes place in ‘middle spaces’
(Fauconnier and Turner 1994). These are subconscious creative events that synthesize
and produce meaning, inference, categories and abstractions from different situations.
They are important because they are invisible and important to the process of bringing
concepts together. They can be articulated and formalized in the process of schema
integration, analysis of suitability for use, transformation and analysis, for example.
Middle spaces can be part of situated tacit knowledge where it is part of practice and
organizational culture. Middle spaces in a KPGIS can adhere heterogeneous
arrangements and alignments that are part of functional use. An important challenge to
working with KPGIS is the presence of middle spaces and the effort to express the work
that takes place there.
KPGIS exists as sociotechnical arrangements
The glue between technology and agency
Latour (Latour 1987; Latour 1996) argues that technical systems are shaped and
sustained through negotiation and alignment of actors. Some examples of actors are
funding, power hierarchies, institutional relationships, information, technology,
disciplinary practices, research subjects, publication, products and ownership.
Change from these actors can be abrupt, have subtle shifts over time, or may arise
individually or in together. Regardless, they represent forces of change driven by the
need to maintain support and resources needed for success.
Conscious questions come with strategies
De Man initiates description of his process to establish a GIS with a section titled
“Information – an answer to a question”. In this he asserts that the purpose of an
information system is to answer questions and these well connected with managerial and
decision making responsibilities. Consequently, the system must be crafted with certain
awareness of the need and means to answer the questions at hand. This is similar to
critics of the data, information, knowledge and wisdom hierarchy who contest the idea of
raw data measured without preconceived recognition of needs (Rowley 2007). The
knowledge function in the model is the origin of questions and demand for information
needs, but it is more than the origin/terminus of a cycle. Questions and strategies to
answer them are inseparable meaning knowledge connects all the model’s functions
directly or through a series of accountable actors. Organization and responsibility are
part of the knowledge function and it is convenient to begin exploration with need to
know because in practice this is what initiates a KPGIS.
The need to start
Traditional approaches to user needs analyses (UNA) are unsuitable for a model of
knowledge production GIS because they do not account for organizational practice and
real time decision making required to meet established objectives. Most user needs
analyses (UNA) start with and assessment of the information needs and move quickly on
to consider integration of the system and organizational development. The steps that
comprise a UNA are:
 Identification of users
 Organizational support
 Definition of required products
 Workflow integration
 Database development
 Develop user applications
 Evaluate costs
(Guptil 1988; Huxhold 1991; Azad 1993; Onsrud and Pinto 1993; Budic 1994)
The attention to systems’ details and organizational preparedness ignores expected
functional use, lived-in use or current and future development of practices. Elements of
the UNA are present in KPGIS but these exist in a nonlinear relationship as the functions
interact. Obvious among these are knowledge, abstraction, database integration, analysis
and products; knowledge is the origin for the need; abstraction is interaction and
integration with ontology; database integration is predicated on suitability; needs must be
appropriate for the end purpose or application.
In addition to Chrisman’s (Chrisman 1987) emphasis that GIS are a response to social
settings
Interorganizational relationships and unanticipated external influences shape problem
questions and information needs.
In 1990 the Mapping Sciences Committee embarked on a substantial review of
the U.S. Geological (National Research Council Mapping Science Committee 1990)
Survey and dealt with the user needs of partner agency.
Worldview and ontology are connected to need to know.
Check Hunter and Beard for user needs regarding suitability.
Identification of user needs for KDGIS are different from these needed for simple
& coupled situations and are motivated by intra and extraorganizational demands. .
MORE REFERENCE TO THE NATURE OF COMPLEX SITUATIONS. Data and
information demand internal to KDGIS can be established by design and program or may
develop in an ad hoc manner to satisfy requirements present in ‘just in time’ analysis and
product production. Pursuit of scientific questions can redefine en situ definitions of
analysis, practice and time requirements (Hesse, Sproull et al. 1993; Michner and Brunt
2000).
Organizational support is minimal compared to traditional GIS UNA. The
support needed is more related to resource expenditures that may be in short supply. This
wraps around to the evaluate costs element. Change in user applications may be the
source for change in user needs rather than something developed later. The same goes
for definition of required products, although the nature of these may remain unchanged,
but the means or constitution may drift. Database development is more likely to be
database integration. Work flow can be of concern but these may not be durable or long
lasting due to frequent change.
ABSTRACTION
This is part of getting needs and worldview together in a productive manner. It can also
be a place where confrontation with others’ abstractions are encountered – a connection
to integration and data reuse.
“Abstractions, as conceptualizations of the world influenced by personal and
social organizations, commonly form the basis of our interpretation of reality.”
(Nyerges 1991 1485)
Production of spatial data requires coordination and alignment of measurements that will
successfully meet organizational information needs.
To articulate information needs and develop or acquire new information or data resources
requires transformation of the organization’s descriptive model and interpretation of
reality into a geographic model. The transformation process needs to account for time,
space and attribute along with the means for their measurement and integration.
Translation requires careful attention to degree of detail in the descriptive model that
must be preserved and accounted for in this process. Implementation of the strategy to
conduct measurement requires practical architecture of a measurement process and
protocol that can deliver on the identified information needs.
Integrate this into the position of relationships among functional elements. Which ones
does it touch, inherit or shape as interactions.
The first step in this process is to condense the descriptive model into a practically
adequate database model. Four abstractions are used to support this action: classification,
generalization, aggregation and association (Nyerges 1991). Abstractions must be
understood as social constructions that represent shared understanding among some or
many individuals in an organization or society. They exist for the convenience and utility
they contribute to model development. The short description of definitions and four
abstractions that follows is a summary of their descriptions and characteristics described
by Nyerges (1991).
Abstractions are tightly attached to concepts, as the basis of meaning for categories and
classes. The meaning of concepts depends on knowledge and understanding of how
entities represent an example of the concepts or can categorized by its properties. Three
ways of constructing meaning for a category (concept) are possible.
 Category by classical definitions come in the form of hierarchical structures with
successive nested classes that have increasingly detailed definitions or classes that
govern membership.
 Category by prototype definitions and classes are characterized by the degree of
similarity to an established archetype. Effective application of prototype classes
depends on detail in the features that define the prototype and serve for
differentiation. Another element is how distinct the features of one prototype from
another so as to insure consistent differentiation
 Category by probabilistic classification employs preponderance of features to
identify entities as members.
Classification is operable when observed characteristics are consistent with an entity
type definition. Each of the three varieties of describing meaning from above can
function to enforce classification through their modes of differentiation.
THIS IS ACCOMPANIED BY SEMANTICS!!
Generalization can be found where subclasses are subsumed under a higher level of
belonging that is less specific or discriminating in definition, i.e. a super-order class. In
such hierarchical arrangements the upper levels are generalizations that include
subclasses with some properties in common. The lower levels are (sub class)
specializations of the super-order class.
Aggregation recognizes multiple component parts that form a unified whole. This is
convenient and practical when integrated units are composed of multiple and sometimes
diverse elements that function together. Large or complex entities often viewed as an
individual intendance of an entity type are held together by aggregation. This is often
convenient when working with networked systems made up of heterogeneous
components that function as a whole. Potable water systems are an example of
aggregation because they consist of everything from the water in a reservoir or aquifer
together with the intricate mechanisms of pumps, pipes, filters, valves and kitchen sink
needed to deliver water to homes. It is practical for arrangements like these to be
aggregated as a definition for an entity type.
Association connects pairs or groups of interactive elements or actions together as a
single entity. Locations that constitute a route is an example as in the traveling salesman
problem. Pairs of origin and destination or before and after change are also associations.
In these cases the constituent elements can be the association but this is only possible
with a form of connectivity among them. Measures of connectivity are frequently an
attribute of associations.
Some wisdom on classification is worth consideration relative to generalization,
aggregation and association as well. Regarding classification, Bowker and Star (1999
p319) state: “Classification does indeed have its consequences – perceived as real, it has
real effect.” If classification has this property, then the same can be said about the
practical use of abstractions to create entity types. Once abstracted, they establish a
linkage between the representative objects or phenomenon with relevant infrastructures,
“…a set of work practices, beliefs, narratives and organizational routines.” (Bowker and
Star 1999 pp319) Following development of accepted abstractions comes the reality of
their application. Real world objects, actions and interactions either find themselves or
vie to be included in the categories or definitions developed through abstraction.
Likewise, something identified as a particular abstraction can experience change or
renegotiation of its character based on the representation, something Bowker and Star
describe as ‘torque’. This is especially sensitive when steps are taken to change or
rearrange the abstractions. The world represented by those abstractions becomes
redefined in the process. Abstractions are powerful and necessary to represent the world
in databases for GIS, but careful attention to the outcomes from their application is
needed to ensure faithful and appropriate representation.
Construction of a geographic model from construction of entity-types is as localized as
development of abstractions.
Nyerges goes on to recognize that ‘formalized abstractions’ are fundamental to
understand the world. Taken together, abstractions are fundamental to structures that
include causal relationships to become an explanatory model. Said explanatory model
includes causal relationships as well as non-causal relationships that provide context.
Development of formalized abstractions and causal relationships can form within a
localized context or they may be adopted from abstractions that have formed from
outside influences. Survival of society is predicated on certain abstractions, causal
relationships and shared understandings of the world. Examples of this are a
legal/political structure and projections. On the other hand, some causal relationships of
a phenomenon in the process of becoming fact can provoke a multitude of opinions.
Acid rain and global warming are classic instances of poorly formalized abstractions. In
these situations, both the relevant abstractions and causal relationships may remain in
question.
SCHEMA
(Nyerges 1989)
What good is schema?
“Conceptual schema of a database specifies the content of a database in terms of the
semantics and structure of metadata rather than the data” p156
Who does the schema?
Get an example of an ER to show what a schema looks like – use Tim’s
‘metadata’ data that describe or define data
1. data descriptors for the data elements that are stored in a database (structural,
coding and data quality)
2. data dictionary with data definitions and the meaning of the elements
Note, meaning is situational
Tim’s emphasis is on structural data descriptions. These come from data needs and
worldview abstractions.
Three GIS environments:
 GIS data transfer
 Database design
 Federated database
Often supported using an ER (Chen 1978)
Tim, 1980 = “A first attempt at using ER diagrams and extensions to it for spatial
database design….” P157
‘Geographic phenomenon model’ then “spatial representation model”
This connects the worldview and abstractions with schema and database model.
Needs, abstraction, schema, database model.
Lodwick and Feutchwanger 1987
DATABASE MODEL
Tim 1989 - schema
Something here relates to abstractions as the basis for the model
Check into the modeling diagram that has all of the database models.
All Tim, Donna, Worboys
MEASUREMENT and TRANSFORMATION
Nick, Nick, Nick, Tobler
Epistemlogy
DATABASE INTEGRATION
Tim 1989 – schema integration
Flowerdew
Check Gary Hunter on user needs & suitability for use
Semantics
Schema integration
Donna ‘essential readings” p250
User needs analysis from
This is traditional practical industrial GIS.
 UNA
o Where does articulation of the need take place
 Top down decision
 Bottom up needs then become top down
 Funding/support
o Conducted






Internal
 If knowledgeable staff
 Requires training, skill or knowledge
 Assumes in-depth knowledge of org practices
 External
 No internal skill
 For planning new capabilities
 Requires intimate understanding of organization operations
Identify the users
o Actual interface with the system
o A user is a person who uses the system for production, or who works with
the products developed by the system.
o Operations – rely on the system as a resource and collect data through
work duties
o The potential user: someone who cannot use but could become a user as
the system develops.
o Product production: maps & reports
o End product users
o Hard/software for delivery of end product
o What models or processes are involved
Define required products
o Based on present and future users
o Identify the characteristics of products being produced
 Types of information
 Format
 Accuracy
 Scale/resolution
o Products need to anticipate products needed to support primary products
o What are the needs of models/processes involved
o Analytical organizations without standardized products and are not
intended for wide circulation ‘pose a more difficult problem’.
Work flow
o Manual or automatic
o Evaluation of current practices
o Consider need for change to integrate with practices
o Disciplinary data practices
o Output media as influenced by the work flow
o Must accommodate accuracy required by practice
o Extension to the data providers and end users is part of work flow
“As this stage the URA manager should have a complete picture of the data
inputs, production processes, data outputs, users, and costs of the existing
system.”
Data Base Development
o The most expensive part of the whole GIS enterprise
o Review of existing data
o Cost for data conversion




o Extraorganizational data acquisition
o Data collection
 Special for database development
 Integration of work practices to populate the database
User Applications
o Transformation of data for suitability
o Compatibility with current models
Refinement of product characteristics
o Reassessment and feedback
o Reduce redundancy
o Adjust output media as needed
Operating costs
Volumes
o Data
o Product
Decker (Decker 2001) lists ‘questions’ about data needs:
 Identify use specifications
 Timeline
 Participants
 Cost
 Region of interest
 Currency / Time
 Expected follow-up
GIS data production projects
o Coordinate
o Specify
o Plan
o Fund
o Build
o Distribute
o Maintain
Chrisman p.243
User needs must address:
Data, process and products
Erik De Man
Introductory readings in GIS p.324-340
Reprint from IJGIS 1988 vol2 245-261
“The value of any information system arises out of the usefulness of its reultant
information products” p325
problem solving
How does user needs develop in a KDGIS?
See Bonham-Carter on ‘custodial’ vs. ‘project based’ GIS. P.10-11
Science:
Hypotheses/theories
Models
Disciplinary requirements
Base data or place based field representation/context
Data to support primary data, model and applications
Resolution, accuracy, currency
Data that must be collected or reused
Proposals
RFP
Funding
Disciplinary practices
DIKW critique
Mode 2 organization characteristics.
What is the divide between Mode 1 and Mode 2
Is this division useful?
Perrow’s typology as a view of KD vs. practical organizational endeavors
http://rwl5.uwc.ac.za/usrfiles/users/99062813/documents/Grosjean_Garnet_187.d
oc,http://www.prescott.edu/faculty_staff/faculty/scorey/documents/NowotnyGibbons200
3Mode2Revisited.pdf,http://www.iamot.org/paperarchive/153B.PDF,http://fp.tm.tue.nl/e
cis/working%20papers/eciswp46.pdf
Knowledge production. Why is it an issue?
“Any ideology that breaks the hold a comprehensive system of thought has on the
minds of men contributes to the liberation of man. Any ideology that makes man question
inherited beliefs is an aid to enlightenment. A truth that reigns without checks and
balances is a tyrant who must be overthrown and any falsehood that can aid us in the
overthrow of this tyrant is to be welcomed. It follows that 17th and 18th century science
was an instrument of liberation and enlightenment. It does not follow that science is
bound to remain such an instrument.” (Fayerabend 1975) p.4 italics in original
THE PURPOSE OF THIS IS TO PAY HOMAGE TO THE PAST AND
IDENTIFY HOW THERE ARE DIVIDES IN PERSPECTIVES ON THE NATURE OF
KNOWLEDGE PRODUCTION. SCIENCE IS PARTICULARLY AVAILABLE
BECAUSE OF THE LONGSTANDING CONCERN FOR ITS PRACTICE AND
RELATIONSHIPS WITH SOCIETY.
The practice of science is inextricably associated with the notion of knowledge
production even though the nature of that relationship is inconsistent. Conflict and
struggle are also inextricable from the times of Aristotle, Copernicus, Galileo the
Renaissance and the Catholic church up to current debate over Global Warming and
Evolution. The depth of this history is interesting and important but is not substantially
important to the current nature and state of knowledge production today; the relationship
between society and science is anything but stable. It is reasonable to connect the thread
of science and knowledge production back to the self titled ‘Philosophers of Science’
circa 1960: Karl Popper, Thomas Kuhn, and Imre Lakatos.
Proponents of these ideas and their critics have helped shaped current
perspectives on science and the production of knowledge.
Posing science as the study of nature –Latour- is a convenient way to contain
science for the purpose of critique. It suspends scientists in their relationships between
their subject and engagement with society. Latour extends this tension to include social
sciences with the assertion that they are bound too tightly to their subjects and practices
for meaningful purpose.
This is a review of the seminal works that ground today’s perspective on science
and by extension the production of knowledge.
I NEED A WAY TO GET KNOWLEDGE PRODUCTION OUT OF THE
HANDS OF SCIENCE… UIRR
Bell describes knowledge as “a set of organized statements of facts or ideas.”
(Bell 1973) p.41
There’s the plethora of definitions from the DIKW lit.
What is the definition of knowledge?
Philosophers of science, social constructionists
Era of Aristotle and ptolmy
Copernicus, Galileo, Newton – all Renaissance dates
Popper, Kuhn and Lakatos are notable because they posed questions and theories that
challenged the acceptance of science as a privileged activity.
The Logic of Scientific Discovery. (Popper 1959)
Popper looked at science as a form of knowledge that progresses by way of the
falsification of theories. CITE ME His concern is for the logic of science.
he centrality and priority of problems in Popper's account of science is paramount, and it
is this which leads him to characterize scientists as ‘problem-solvers’. Popper had
presented falsificationism as a way to overcome the problem of induction and also to
distinguish scientific from non-scientific propositions. Popper’s prescription implies a
smooth progress from one hypothesis to another as they are falsified and replaced with
increasingly bold and powerful hypotheses.
The structure of scientific revolutions (Kuhn 1962)
Kuhn noted that historically, science seems to exist in two modes–periods of “normal
science” when scientists use existing knowledge to solve problems and periods of
revolution when the existing knowledge itself is called into question. This is a question
into the history of science.
Kuhn had described science as consisting of periods of normal science interspersed with
periods of great conceptual change, backing up his case with evidence from the history of
science.
Kuhn's approach to the history and philosophy of science has been described as focusing
on conceptual issues: what sorts of ideas were thinkable at a particular time? What sorts
of intellectual options and strategies were available to people during a given period?
What types of lexicons and terminology were known and employed during certain
epochs? Stressing the importance of not attributing modern modes of thought to historical
actors, Kuhn's book argues that the evolution of scientific theory does not emerge from
the straightforward accumulation of facts, but rather from a set of changing intellectual
circumstances and possibilities. Such an approach is largely commensurate with the
general historical school of non-linear history.
The paradigm shift does not merely involve the revision or transformation of an
individual theory, it changes the way terminology is defined, how the scientists in that
field view their subject, and, perhaps most significantly, what questions are regarded as
valid, and what rules are used to determine the truth of a particular theory.
On the one hand, logical positivists and many scientists have criticized Kuhn's
"humanizing" of the scientific process for going too far, while the postmodernists,
together with Feyerabend, have criticized Kuhn for not going far enough.
(Lakatos 1976)
Lakatos saw science as an inherently social activity. He claimed that theories thrive only
when an active research community keeps them in circulation. Lakatos attempted to
explain Kuhn’s work by arguing that science progresses by the falsification of research
programs rather than the more specific universal statements of naïve falsification. In
Lakatos' approach, a scientist works within a research program that corresponds roughly
with Kuhn's 'paradigm'. Whereas Popper rejected the use of ad hoc hypotheses as
unscientific, Lakatos accepted their place in the development of new theories.
The problem for Lakatos was to defend the presumed rationality of scientific method
against the apparent impulsiveness of scientists. For Lakatos, science progressed by
developing complex research programmes that include testable hypotheses, and also an
untestable ‘core’ of doctrine, which those involved in the research programme would not
permit to be falsified.
A research programme (or program) consists of, in Lakatos' terms, a negative heuristic or
'hard core' that is not open to negotiation, and in effect lays down the foundations of the
programme. One example given is Newton’s three laws of dynamics, which define
quantities such as force. These are not open to falsification within the Newtonian system,
but are defended at all cost by the positive heuristic, a 'protective belt' of statements that
are open to falsification. When falsified, these are replaced by variations that are also
falsifiable, but which continue to protect the hard core. Thus a research programme
provides a framework within which research can be undertaken with constant reference to
presumed first principles which are shared by those involved in the research programme,
and without continually defending these first principles.
Lakatos claimed that research programmes could be evaluated by comparing their ability
to produce new facts, and by their ability to explain apparent refutations. In effect, a
research programme grows as its positive heuristic extends its applicability into new
areas. A research programme that is in a state of constantly defending its hard core, and
which appears not to be extending itself into new areas, becomes degenerate. Such
research programmes are in danger of being superseded by more vigorous competitors.
Karl Popper. Popper looked at science as a form of knowledge that progresses by way
of the falsification of theories. (Falsification is also referred to as refutation).
Popper emphasized the epistemological impossibility of ever proving a theory
correct, but insisted that the act of proving a theory incorrect contributes to our
knowledge of nature.
Thomas Kuhn. Probably the most influential philosopher of science. (Cf. The Structure
of Scientific Revolutions, 1963). Kuhn noted that historically, science seems to
exist in two modes–periods of “normal science” when scientists use existing
knowledge to solve problems and periods of revolution when the existing
knowledge itself is called into question. Kuhn used the word “paradigm” to
describe the shared knowledge, tools, and concerns of a scientific community.
During normal science, scientists use the paradigm to solve problems. Inevitably,
anomalies arise–observations that can’t be explained within the paradigm. When
there are two many anomalies, a crisis develops which leads to a revolution
(which he also referred to as a paradigm shift). During revolutions, the paradigm
is attacked and a new, different paradigm emerges.
Imre Lakatos. Lakatos saw science as an inherently social activity. He claimed that
theories thrive only when an active research community keeps them in circulation.
Good theories can thus fade when their support subsides, and bad theories can be
promulgated by the support of enough scientists.
Paul Feyerabend examined the history of science with a more critical eye, and
ultimately rejected any prescriptive methodology at all. He rejected Lakatos’ argument
for ad hoc hypothesis, arguing that science would not have progressed without making
use of any and all available methods to support new theories. He rejected any reliance on
a scientific method, along with any special authority for science that might derive from
such a method. Rather, he claimed that if one is keen to have a universally valid
methodological rule, epistemological anarchism or anything goes would be the only
candidate. For Feyerabend, any special status that science might have derives from the
social and physical value of the results of science rather than its method.
A transition out of the classic science as knowledge production comes with
Gibbons and the ‘transformation of knowledge production’ that amounts to the
transdisciplinary shift out of the university setting and into the world of commerce. The
STS folks had a lot to say about this and I need to capture that. Their perspective is the
place that connects STS with Mode1/2. This will also illuminate the essential tracks that
STS has pursued toward better understanding of science and knowledge production.
Molecular diagnostics incorporates the activities of disparate fields in
addition to physics, chemistry and biology, including informatics and legal and
business activities such as commercialisation, marketing and patenting (Gibbons,
Limoges, Nowotny, Schwartzman, Scott, & Trow, 1994). This transformation in
knowledge production was referred to in Chapter 1, as a shift in the mode of
knowledge production from Mode 1, specialist, disciplinary, scientific knowledge
produced in research universities, to Mode 2, transdisciplinary knowledge produced
by collaborations between universities, private industries, government research
institutions, and the sites of application such as hospitals (Gibbons et al., 1994).
Accompanying this transformation in knowledge production it was also noted that
there is a blurring of boundaries between the pure and applied sciences, and between
clinical pathology and medical science research.
Does STS go here?
How is attention to UIRRs the natural extension of the science wars, or is it?
Gibbons, Nowotny et al wade in circa 1994 on the UIRR track. Lots of flack from the
STS community follows. And Mode 2 circulates among the actors and is valued for the
line it draws not just in relations to the UIRR theme, but as a characterization of
knowledge production in general.
The co-evolution of science and society has led to increased complexity, unpredictability
and irregularity in both spheres. Post-modern society has both a new perception of
uncertainty and new means of dealing with risks.
Merton. Kuhn. Giern (How science gets rhetorically constructed as a cultural space.)
What is knowledge and how has it been constructed – what knowledge is
(definition) and the means of production/creation.
There’s a relationship between knowledge, society, culture and power. How is
this related to my concerns.
Gibbons et. al. are concerned with the character of higher education and
relationships with knowledge. I need to verify how well connected.
The scrutiny on higher education is useful because it forces recognition that
University settings, science and Mode 2 can co-exist.- This is Garnet’s position.
How can ‘traditional science’ be differentiated from the new Mode 2 knowledge
production – Garnet addresses this and Musson.
Where did Gibbons and Nowontly come from? What is the motivation for their
assertions? What complaints are they trying to air?
Gibbons et al (1994) assert that Mode 1 knowledge production takes place within
disciplinary communities where the outcomes are consumed. This observation is a
pointed critique of the relationships among higher education, research, academic agenda,
peer review, and embedded career paths. Knowledge is legitimized by these relationships
in the scientific disciplines. In contrast, Mode 2 knowledge production are solutionfocused endeavors related to the analysis of problems and designing solutions. MORE
HERE TO CONTRAST MODE 1 WITH MODE 2.
Gibbons 1998 p54 argues that linkages between Mode 1 and Mode2 are needed to
satisfy national and social needs for knowledge production. This
THE POSITION OF SCIENCE RELATIVE TO SOCIETY:
Investigators of the social systems of science have explored its organization and
relationship with society. Merton (Merton 1972) postulates that Science is "an organized
social activity of men and women who are concerned with extending man's body of
empirical knowledge through the use of these techniques. The relationships among these
people, guided by a set of shared norms, constitute the “social characteristics of science".
This conclusion comes a decade after publication of his principle norms governing
science activity: (1) the scientist is expected to evaluate new knowledge critically and
objectively, (2) he is expected to use his findings in a disinterested fashion, (3) scientific
merit should be evaluated independently from the personal or social qualities of the
individual scientist, (4) the scientist does not own his findings; secrecy is forbidden, (5)
he is expected to maintain and attitude of emotional neutrality toward his work. (Merton
1957; Barber 1962).
The success of these norms to represent, advance and create a positive opinion of
science work is reflected inside and out of the scientific community. At the time Merton
and Barns published their works the scientific community believed that the success of
science and technology could be attributed to these ideals (Price 1962). Distance from
personal emotion and prejudices in adherence with the Merton and Barns’ ethos is seen as
a contribution to popular perception of the trustworthy integrity of science (Ben-David
1975). Protection of the scientific community from social criticism is furthered by
reification that these norms reflect the positions and practices adopted and exercised in
pursuit of science (King 1971). It is no longer a surprise that this favorable perception of
normal science would experience a paradigm shift. Thomas Kuhn’s (Kuhn 1962)
observation that science is regularly subject to inquiry and subsequent crisis. That these
paradigm shifts are irregular and resulting redirection is contingent opened questions on
the rational progress and stability of normal science (Hacking 1999) p. 97-8. The
ideology of objective problem solving where success is not predetermined by reason or
wisdom was later co-opted by postmodern thinkers and social constructivists that
questioned truth seeking scientific authority.
The social constructivist tradition dates back to Fleck’s publication of “Genesis
and Development of a Scientific Fact” (Fleck 1935/1979).
Pragmatist realist. I know ANT is radical realism
Handbook p71
Critical theory in relation to the dominant liberal ideology of science
Habermas “technology and science as ideology” Habermas 1971
Feyerband 1978 “anything goes”
Frankfurt School Marxism: Marxist science studies have critique of neutrality
neutrality of
Langdon Winner “Autonomous technology” 1978
Feminist
STS today is increasingly concerned with how to theorize and make practical
structures of public participation in scientific and technological decision-making and
design (Kleinman 2000)
Berger and Luckman suggested that science moves ‘away from’ past practice not
‘toward’ a well-designed strategy of exploration. The ‘Strong Program’ advocates,
Barnes (Barnes 1977; Barnes 1995) and Bloor (Bloor 1976), were much more visible
with questions about the stability of the content of science (Hacking 1999) p.65. The
Science, Technology and Society field emerged from the works of dedicated iconoclastic
constructionist advocates such as…..???? Shapin (Shapin 1996) is adamant that science
can only be understood in relation to its context of occurrence.
MORE ABOUT THE PRIVILIDGED POSITION OF SCIENCE AND THE
SOCIAL CONTRACT.
WHAT ABOUT KNOWLEDGE – WHAT DOES THIS SAY ABOUT THE
PRODUCTION OF KNOWLEDGE – FUJIMURA’S WORK IS SPECIFICALLY
KNOWLEDGE BASED. LATOUR AND WOOLGAR, POLITICS OF NATURE AND
FACTS , SCIENCE ON THE RUN. THE HANDBOOK.
LUCKMAN ON SOCIAL CONSTRUCTION OF REALITY (SISMUNDO P
52). GALILIEO, BOYLE AND HOBBES STRUGGLE TO MAKE THEIR
KNOWLEDGE KNOWN – PACTS WITH SOCIETY. SHADES OF LATOUR IN THE
JANUS. CASPER AND CLARKE – PAP SMEAR AS A TECHNIQUE & RELATED
CONSTRUCTION OF KNOWLEDGE. IN THIS CASE IS IT A PRACTICED BASED
KNOWLEDGE/REALITY/STABILITY? ARAMIS – A MIX OF ACTORS, BUT IS IT
ABOUT KNOWLEDGE CONSTRUCTION? CALLON’S FISHERMEN – THEY
WERE PART OF AN EXPERIMENT THAT WAS MEANT TO PRODUCE
SCIENTIFIC FACT/KNOWLEDGE. WHAT ABOUT APPLIED SCIENCE VS.
THEORETICAL? SEE THE DEBATES OVER CLASSIFICATION OF SCIENCE AS
APPLIED OR NOT (MODE ½)
SEE PANDORA’S HOPE CHAPTER 1 AND 7 ON THE SCIENCE WARS.
This really needs to recognize the tenets of knowledge production within and
outside of science. Science is a convenient place to be because of the lengthy history of
development of thought over its makeup and relationships with society.
STS has a mission that comes out of the tradition(s) inspired by the philosophers.
This is the place where power and hierarchy come into play (Latour, Foucault).
Whose science, whose wars and whose knowledge?
Science Wars: This began the inquiry into the relationship between culture,
society and science. What was the motivation and dividing lines in the ‘science wars’?
Postmodern science – what does Latour have to say with ‘Nature’?
“Questions and what counts as knowledge need to be examined in terms of
practice, institutions, people, funding and language” (Haraway Quoted in McMillen, L.
“Science wars flare”, A13)
By tricking the journal Social Text into publishing a nonsensical essay applying
postmodern thought to quantum physics,
An incendiary hoax article on postmodern thought and quantum physics
fabricated by Alan Sokal in the journal Social Text was a formative event that initiated
the ‘science wars’ (McMillen 1996).
showed that the cultural study of science was intellectually suspect and ignorant
of the science it purports to study.
Introduce the social constructivists:
The ‘science wars’ is an inevitable standoff between postmodern and
poststructuralist thinkers and Science over epistemology, truth and social construction.
An article written by physicist and professed Leftist Alan Sokal (Sokal 1996) published
in the journal Social Text misrepresented and angered both . “Transgressing the
boundaries: towards a transformative hermenutics of quantum gravity” was a hoax that
misrepresented Science and the field of science studies. That a hoax article was
published
The term ‘science wars’ came out of journalists’ coverage of science issues and
the tense engagement suggested by the phrase invited considerable attention (Latour
2004) p.228.
“Good scientists enlist in the science wars only in their spare time or when they
have retired or run out of grant money, but others are up in arms day and night and even
get granting agencies to join in their battle.” (Latour 1999) p. 19
“pursuit of an autonomous and isolated science”
the difference between research and Science” Pandora’s p20
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