Bibliographic record
Abstract
Lively debate has surrounded the emergence of geographic information systems (gis) as a formidable presence in both intellectual and applied geographic circles. Earlier discourses that polarized gis into two mutually exclusive camps — neutral, objective tool vs. positivist, theoretically corrupt weapon — have more recently been tempered through the infusion of conceptual vantage points such as feminist theory and theories of science as socially constructed practice. The widening array of uses to which gis is now put, including everything from missile sitings and gerrymandering to movements for social and environmental justice, make it even more imperative to situate gis inquiry within broader frameworks that can encompass the richly contradictory cultural, political, and economic landscapes of technology. In this paper, I home in on a case study of a local, fledgling public participation gis (ppgis) effort in order to understand gis as part of the longer trajectory of people's struggles with and against the machine within industrial capitalism. Specifically, I draw from utopian studies to propose that gis can be seen as a contemporary manifestation of the utopian impulse, where technology is both the problem and, when inserted into more emancipatory social settings, the potential cure. The loosely organized collection of people working locally to use gis, in small and often disconnected ways, to interfere in the fabric of industrial (and post-industrial) capitalism in fact represents a utopian undertaking to confront geographically specific problems and create the "better life in the better place."
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.073 |
| Scholarly communication | 0.017 | 0.023 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".