Vulnerability to injury: assessing biophysical and social determinants of land-user injuries in Nunavut, Canada
Bibliographic record
Abstract
Injury is the leading cause of death for Canadians aged 1 to 44 and disproportionately impacts indigenous populations. Similar to chronic and infectious disease, risk of injury is shaped and molded by biophysical and social contexts. This thesis develops an approach to better understand determinants of injury, delineating between distal and proximal causes. The approach is empirically applied to understand determinants of land-use injuries in the Inuit territory of Nunavut. In the region, traveling on the land and sea for hunting is important for food security, health, and identity. Over the past decade however, rates of search and rescue (SAR) have more than doubled. The thesis work examines biophysical aspects of risk through a quantitative analysis of environmental conditions and SAR events during the years 2013 and 2014 in Nunavut. Social and biophysical causes of injury are examined through interviews in three communities. We identify correlations between temperature and ice conditions with SAR events; this relationship is observed as being a proximal cause of vulnerability to injury, while socioeconomic status, land use knowledge and experience, and land use practices are seen to be more distal or ultimate determinants of risk.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".