Resisting geosurveillance: A survey of tactics and strategies for spatial privacy
Bibliographic record
Abstract
Geosurveillance is continually intensifying, as techniques are developed to siphon ever-increasing amounts of data about individuals. Here we survey three tactics and three strategies for resistance in an attempt to provoke greater discussion about resistance to geosurveillance. Tactics explored include data minimization, obfuscation, and manipulation. Strategies for resisting geosurveillance build upon other forms of resistance and include examination of the assumptions of geosurveillance, investigating privacy-focused software alternatives, and strengthening the ability of activists to operate in this sphere. Individually, each of these are unlikely to effect great change; used in concert, they have the potential to guide technological development in such a way that it is less likely to serve corporate and government interests and more likely to protect individual and group privacy.
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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 | 0.027 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.013 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".