Long-Range Transport of Information: Are Arctic Residents Getting the Message about Contaminants?
Bibliographic record
Abstract
Since contaminants were discovered in Arctic human populations well over two decades ago, northern residents have been receiving information about the nature of such contaminants in the environment and their possible effects on human and wildlife health. The information offered has evolved with attempts to improve its sensitivity and appropriateness and to assure northern peoples that traditional foods are still a healthy choice. A survey conducted in four Nunavut and Labrador communities to evaluate the degree to which residents had been exposed to and comprehended information regarding contaminants in country food found that the information has not been as broadly received as expected. In particular, women of childbearing age—a key population group—do not appear to have understood or to be able to recall messages previously disseminated. We argue the enormous effort put into communication on contaminants is not achieving the desired result: the statements and actions of Arctic people do not reflect the importance of the information passed on through communication programs. Characteristics of risk communication, as well as those of Arctic communities, may be influencing how information is received and interpreted. Much recent dissemination of information about country foods in the Canadian Arctic has emphasized the nutritional value of such foods. Should it become necessary to “nuance” this message in the future, regarding certain species that are being consumed or certain population groups with higher risk of contaminant exposure, it appears that more effective communication modes and messages will need to be developed.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".