Bibliographic record
Abstract
Residents of pollution hotspots often take on projects in ‘citizen science’, or popularepidemiology, in an effort to marshal the data that can prove their experience of the pollution to the relevant authorities. Sometimes these tactics, such as pollution logs or bucket brigades, take advantage of residents’ spatially ordered and finely honed experiential and sensory knowledge of the places they inhabit. But putting that knowledge into conversation with law requires them to mobilize a new, ‘foreign’ set of tools, primarily oriented to the observation, measurement and sampling of pollution according to conventional scientific standards. Here, I employ qualitative empirical methods in two case studies of communities ‘downwind’ of Canada’s contested tar sands region to demonstrate that the knowledge that is crucial to these citizen science strategies is not only local, situated and experiential in origin but also collectively generated and held. I draw on the notion of transcorporeality, emanating from feminist theory of the body, to demonstrate that the knowledge offered to law through these efforts often represents a fluid merger of experiential and conventional ways of knowing, posing a productive challenge to the strictly positive notions of science and evidence dominant in legal proceedings.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".