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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 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 Azad, B. (1993). Organizational aspects of GIS implementation: Preliminary results from a dozen cases. 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