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Record W2056654355 · doi:10.1162/0747936041423217

Environmental Information Systems as Appropriate Technology

2004· article· en· W2056654355 on OpenAlexaboutno aff
Kim Fortun

Bibliographic record

VenueDesign Issues · 2004
Typearticle
Languageen
FieldEngineering
TopicWater resources management and optimization
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceEnvironmental planningEnvironmental resource managementBusinessSystems engineeringData scienceEngineeringGeographyEnvironmental science

Abstract

fetched live from OpenAlex

Environmental information systems-involving databases, computer modeling, remote sensing, GIS applications, and a host of other technologies-are now being developed around the world to address a range of issues, from climate change to loss of biodiversity, to economic underdevelopment.' The implications for the natural environment, human welfare, and democratic governance are significant. Environmental information systems structure what people see in the environment, and how they collaborate to deal with environmental problems. They shape scientific inquiry, legal argument, and how citizens participate in governance. They are technologies designed to produce new truths, new social relationships, new forms of political decision-making and, ultimately, a renewed environment. I will discuss one particular environmental information system, an interactive Website supported by a relational database that contains profiles of more than 6,800 chemicals. Maintained by the Environmental Defense Fund, and called the Website integrates pollution information for the United States with information on health risks, and with information on relevant environmental regulations. It allows users to produce customized reports, and encourages communication with the U.S. Environmental Protection Agency, or with a polluting company. A Canadian version of Scorecard went online in April 2001, and a Japanese version is in the planning stage.2 Scorecard could become a technology that is transferred to countries around the world. My main argument is that Scorecard is an example of an appropriate environmental information system-designed in a way attuned to the material, political, and technological realities with which it works, and to the social actors who will be its users. The argument builds on the concept of appropriate (or intermediate) technology popularized in the 1970s, with roots in Gandhian critiques of mass production articulated during the Indian independence movement.3 Advocates argued that, in order to be appropriate, technology should be designed to fit into its setting, synchronized with available material resources, expertise, and labor time. I observed many such technologies in India while conducting field research in the early 1990s, and learned to appreciate how they could combine function with social, technical, and environmental sustainability. I also learned that local settings were inevitably punctured by flows of ideas, people, and goods from elsewhere; with For examples of work on these topics in STS, see G. C. Bowker, Biodiversity Datadiversity, Social Studies of Science 30:5 (2000): 643-684; P. Edwards, Global Climate Science, Uncertainty and Politics: Data-laden Models, Model-Filtered Data, Science as Culture 8:4 (1999): 437-472; R. E. Sieber, Computers in the Grassroots: Environmentalists, GIS and Public Policy(Ph.D. Dissertation, Rutgers University, Department of Geography, 1997); D. Sarewitz, R. Pielke, Jr., and R. Byerly, Jr., eds., Prediction: Science, Decision-Making and the Future of Nature (Washington, DC: Island Press, 2000). 2 The Canadian version of Scorecard, once at www.scorecard.org/pollutionwatch, has been taken off the Web. I do not yet know the reasons. Bill Pease, the designer of Scorecard, mentioned the Japanese version in an interview with Erich Schienke in October 2001. 3 See E. F. Schumacher, Small Is Beautiful: Economics as if People Mattered (New York: Harper & Row, 1973). For a recent analysis that highlights the need for technology to match both users and needs in both complexity and scale, see B. Hazeltine and C. Bull, Appropriate Technology: Tools, Choices and Implications (New York: Academic Press, 1999).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.025
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.007
Science and technology studies0.0040.017
Scholarly communication0.0250.031
Open science0.0030.010
Research integrity0.0110.008
Insufficient payload (model declined to judge)0.0230.011

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.006
GPT teacher head0.169
Teacher spread0.164 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations23
Published2004
Admission routes1
Has abstractyes

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