Systematic Review and Critique of the Contributions of Traditional Ecological Knowledge of Beluga Whales in the Marine Mammal Literature + Supplementary Appendix Table (See Article Tools)
Bibliographic record
Abstract
In this study we systematically review and critique literature containing Traditional Ecological Knowledge (TEK) of the beluga (Delphinapterus leucas) as a case study to gain insights into TEK’s contributions to the marine mammal literature over the past four decades. We reviewed multiple searchable online databases, collected both academic and grey literature, and categorized it by geographic and disciplinary focus, as well as by the contribution of TEK to the source. Of the total 137 papers retained in the final analysis, 67% referred to the Canadian North, particularly the Hudson Bay subregion. Articles that included informal or anecdotal representations of TEK of belugas were the most prevalent. The number of papers containing TEK of belugas increased rapidly between 1975 and 2004 but appears to have leveled off since then. Biological papers represented the largest disciplinary focus (72%), followed by papers on management or co-management. This review showed that although knowledge of Indigenous peoples has made substantial contributions to the understanding of beluga, there is a lack of explicit collection, documentation, and use of TEK in the literature on belugas and particularly in the literature on beluga management.
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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 | 0.072 | 0.223 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.048 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| 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".