Environmental Information Systems as Appropriate Technology
Pourquoi ce travail est dans la base
Une base qui oublie comment elle a trouvé un travail ne peut pas être vérifiée. Voici les voies qui ont admis celui-ci.
Notice bibliographique
Résumé
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).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle