Environmental Information Systems as Appropriate Technology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it