Mechanism Matters: Data Production for Geosurveillance
Bibliographic record
Abstract
Recent revelations of dragnet surveillance by governments around the world have brought attention to privacy and surveillance in their many forms. In this article, we outline the technical mechanisms of geosurveillance to synthesize and inform on a constantly moving target. Despite their interconnections and overlap, to simplify and elucidate these geosurveillance mechanisms, we classify them into three parts: geolocation, unique identification, and the surveillance medium. We show that together they constitute a language that we, as subjects, did not choose yet are increasingly forced to negotiate. Moreover, these mechanisms are both numerous and highly complex and are only one component within large ecosystems of geosurveillance, making privacy ever more evasive. Understanding the mechanisms of our own subjection is integral to any prospects for intervention, however. As such, we highlight the Tor network as an example of resistance to geosurveillance that is enabled by acutely understanding the hypertechnical language that otherwise binds us. Indeed, as we emphasize throughout, mechanism matters.
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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 | 0.039 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.024 | 0.037 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.015 | 0.006 |
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".