Bibliographic record
Abstract
Every once in a while, an outstanding work of scholarship comes along that transforms the way a seemingly intractable injustice is seen and, in so doing, also transforms the way it should be approached and addressed by all concerned.Such a work is Everyday Exposure: Indigenous Mobilization and Environmental Justice in Canada's Chemical Valley by Sarah Marie Wiebe.The injustice is the systemic social and ecological suffering of Indigenous peoples and their communities within the jurisdictions and policies of the Canadian federation.She shows how this unjust system persists and deepens despite well-meaning attempts to address it in what is perhaps the worst case: the horrendous "slow violence" of health and ecological suffering of the Aamjiwnaang First Nation surrounded by Chemical Valley.In meticulous detail, she delineates the complex system or assemblage of private and public law, power relations, different types of knowledge, ambiguous jurisdictions, history of treaty making, geopolitical interests, consultations, deliberations, partnerships, protests, reviews, and differentially situated actors in which policies are developed and applied.With this multilayered policy assemblage in clear view, she shows precisely how it repeatedly fails to generate and enact policies that effectively address either the unregulated production of petrochemical and polymer toxins and pollutants that devastate the lives and homeland of Aamjiwnaang citizens or the ongoing intergenerational human harms and ecological devastation to Aamjiwnaang citizens and their home.Sarah Marie Wiebe developed a unique method to carry out this research.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.568 | 0.492 |
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".