Bibliographic record
Abstract
Inuit and scientists are increasingly aware of the presence of contaminants in the Arctic food web and of the threat these contaminants pose to human and environmental health and well-being. The varied ways that Inuit think about and react to contaminants in the foods they eat are explored in a case study of one Inuit community: Arviat, on the northwest coast of Hudson Bay. Reactions to contaminants are mixed. While Inuit are informed of scientific findings through a variety of media, they also have their own explanations for the changes that are taking place in the animals on which they rely. This study juxtaposes global cause and effect, as understood by the scientific community, and the local causes and effects of contamination as understood by Inuit. The Inuit of Arviat are incorporating contaminants research into their hunting practice and earning money by collecting tissue samples and hosting southern researchers. This typical Nunavut community demonstrates the heterogeneity of understanding that exists and the ways in which local people are turning something very negative to their advantage.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.008 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 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".