The adaptation of the beyond cold water bootcamp course for Inuvialuit communities in Northwest Territories, Canada
Bibliographic record
Abstract
Boating-related fatalities in the Northwest Territories (NWT) are well above the national average. These fatalities are exacerbated by very cold water, and water and boating safety resources that lack relevance to residents of northern communities. We utilised an iterative, participatory approach to create a plain language, culturally and geographically adapted version of a cold water survival course, "Beyond Cold Water Bootcamp". The purpose of this research was to determine what adaptations are necessary to create appealing and pertinent boating safety interventions for Inuvialuit communities in the NWT and to demonstrate the value of generating such interventions. First, we conducted a focus group with boating safety experts to gain feedback on the first draft of the adapted course. We then subsequently completed a pilot of the course with community members in Tuktoyaktuk, NWT, and we obtained their feedback and suggestions. We then trained a local community member to deliver the course and conducted another focus group with residents. Using reflexive thematic analysis, our results demonstrated the value of culturally and geographically adapted boating safety interventions for Inuvialuit communities and the importance of relinquishing colonial power structures and enabling community members to independently adapt and disseminate knowledge.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".