Challenges and strategies when mapping local ecological knowledge in the Canadian Arctic: the importance of defining the geographic limits of participants’ common areas of observations
Bibliographic record
Abstract
Traditional and local ecological knowledge (TEK/LEK) are important sources of information for wildlife conservation. However, there are often limitations and biases in the TEK/LEK methods used. In this study, we examined and implemented strategies to address the limitations and biases we identified while analyzing the mapped observations collected from 27 interviews as part of a larger project on walruses in Nunavik (Canadian Arctic). Our main objectives were to: (1) examine the importance of recording participants’ temporal and spatial limits of observations; (2) identify the factors influencing the quantity and diversity of mapped observations; (3) study the importance of documenting approximate numbers of animals observed; (4) examine the importance of gathering and presenting data at consistent and standardized spatial scales. We found that by adding to maps the geographic limits of participants’ common areas of observations, we were able to distinguish areas that hunters typically visited and did not see walruses, from areas that hunters never visited. Furthermore, we showed that the variability in the quantity of mapped observations was explained by the community of residence and average number of hunting trips per participant, but not by participant age. Finally, although careful adjustments and standardization would be needed, we showed that by having an estimate of the number of walruses observed per area drawn, it would be possible to estimate mean local abundances of walruses. We hope this careful examination of TEK/LEK methods will help to increase confidence in these datasets as a valuable source of knowledge for wildlife conservation.
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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 | 0.126 | 0.204 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.014 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.006 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".