Conversation: Surveillance / Environment / Nature / Sustainability
Bibliographic record
Abstract
One of the key areas that surveillance studies has been conspicuously little involved in, with only a few exceptions (Donaldson and Wood, 2004; Donaldson 2012; Ottinger 2010; Haggerty and Trottier 2015; Archer 2021), has been the environment and nature. As it becomes increasingly obvious that the effects of the climate crisis are already with us, and with biodiversity loss accelerating, and the environmental justice issues associated with these crises and the potential responses to them worryingly unaddressed, it seems clear that surveillance studies should have more to say. In this free-form, wide-ranging discussion, Simone Browne, Francisco Klauser, and David Murakami Wood, three leading surveillance studies scholars who’ve all been involved in the field and the journal for most, if not all, of its history, discuss the different ways in which their research is dealing with questions of environment, nature, and sustainability and how surveillance studies more broadly could engage. Each starts by introducing their current research direction, before the conversation opens up.
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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 | 0.018 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.012 |
| Scholarly communication | 0.012 | 0.024 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.027 | 0.031 |
| Insufficient payload (model declined to judge) | 0.021 | 0.005 |
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".