Science, Data, and the Struggle for Standing in Environmental Governance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it