Theory, Practice, and History in Critical GIS: Reports on an AAG Panel Session
Bibliographic record
Abstract
Extending a special session held at the 2008 annual meeting of the Association of American Geographers in Boston, this commentary collection highlights elements of the critical GIS research agenda that are particularly pressing. Responding to a Progress report on critical GIS written by David O'Sullivan in 2006, these six commentaries discuss how different interpretations of ‘critical’ are traced through critical GIS research. Participants in the panel session discussed the need for a continued discussion of a code of ethics in GIS use in the context of ongoing efforts to alter or remake the software and its associated practices, of neo-geographies and volunteered geographies. There were continued calls for hope and practical ways to actualize this hope, and a recognition that critical GIS needs to remain relevant to the technology. This ‘relevance’ can be variously defined, and in doing so, researchers should consider their positioning vis-à-vis the technology. Throughout the commentaries collected here, a question remains as to what kind of work disciplinary sub-fields such as critical GIS and GIScience perform. This is a question about language, specifically the distance that language can create among practitioners and theoreticians, both in the case of critical GIS and more broadly throughout GIScience.
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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 | 0.113 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.045 | 0.019 |
| Scholarly communication | 0.017 | 0.014 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.030 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".