Science, Data, and the Struggle for Standing in Environmental Governance
Bibliographic record
Abstract
Here, we explore how people entangled in natural resource conflicts employ and discuss data. We draw on ethnographic research with two cases of conflict: salmon fisheries in Alaska, USA, and agricultural water management in Saskatchewan, Canada. Both cases illustrate how data, through the scientization of environmental governance, can become a means of empowerment and disempowerment: empowering those with access and influence over data and disempowering those without such access. In both locales, people find it necessary to perform their expertise, justify the veracity of their data, and discount the data held by others if they wish to achieve or maintain standing. We call this “datamentality” and draw lessons from these cases for how we can structure environmental governance such that it benefits from robust data and science while meeting the needs of individuals, avoiding or managing power struggles, and protecting the rights of all involved.
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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 | 0.058 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.034 | 0.141 |
| Scholarly communication | 0.022 | 0.026 |
| Open science | 0.002 | 0.025 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".