Bridging Cree knowledge and Western science to understand the decline in hunting success of migratory Canada geese
Bibliographic record
Abstract
Abstract Canada goose (Branta canadensis) is one of the main waterfowl species harvested by Cree hunters in James Bay, Canada. Land users who hunt geese along coastal Eeyou Istchee (Eastern James Bay, Quebec) report that they are now much less successful in harvesting sub-arctic breeding geese (B. c. interior) than in the 1980s, especially during the fall hunting season. We followed a mixed-methods triangulation design in which we simultaneously gathered Indigenous and scientific knowledge. For the Indigenous knowledge, we conducted semi-structured interviews with Cree land users who shared their knowledge about how the goose populations that stage in Eeyou Istchee have changed within living memory. They attributed their reduced hunting success to fewer migrating geese and modification of their behavior. They also identified many environmental changes, especially the decline of eelgrass (Zostera marina), that may have affected the number, distribution, and migration patterns of Canada geese along the coastal Eeyou Istchee in the past 50 years. We complemented this information using waterfowl study techniques including aerial surveys, band recovery analyses, and GPS tracking of individually marked geese. Habitat changes both at the local scale in Eeyou Istchee and in other parts of the staging and wintering ranges of Canada geese, natural and human disturbances along the coast, and a gradual increase in molt migrant temperate breeding Canada geese (B. c. maxima) likely resulted in changes in habitat use and migration patterns of sub-arctic breeding Canada geese along the James Bay east coast. By bridging Cree knowledge and Western science, we identified the various factors that affect the harvest success of Eeyou Istchee goose hunters. Such an approach should be encouraged when Indigenous peoples rely upon migratory bird or mammal species that spend only a portion of their annual cycle within the hunting territories of land users.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".