Strategic translation: pollution, data, and Indigenous Traditional Knowledge
Bibliographic record
Abstract
Abstract This essay examines the role of data practices in the making and refuting of settler colonial environmental science. Investigating the epistemic contestation surrounding environmental contamination produced by the oil industry in Alberta, Canada, I discuss an alternative approach to toxicology: a community‐based monitoring programme that uses a ‘three‐track’ methodology to present data in three distinct forms. Using this method, First Nations communities engaged in strategic translation, balancing their aim of rendering Traditional Knowledge and community needs legible to policy‐makers against their desire to protect Traditional Knowledge from being assimilated into the dominant data paradigm. This translation, I argue, enacts a form of resistance in an era of relentless datafication: making‐things‐into‐data can reflect the exercise of agency rather than submission to external pressure. In this way, the three‐track methodology models how marginalized communities can leverage data's productive capacities for their own ends and produce scientific knowledge on their own terms.
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.
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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.077 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.004 |
| 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".