Elders’ Voices: Examples of Contemporary Indigenous Knowledge of Marine Mammals
Bibliographic record
Abstract
The legal and moral imperatives for incorporating indigenous knowledge into natural resource management are now widely recognized. Many consider the integration of indigenous knowledge to be an essential component of successful solutions for conserving resources valued by indigenous peoples, including marine mammals. The effective integration of indigenous knowledge requires an understanding of what it is. Indigenous elders from five very different parts of the world briefly explain their knowledge of local marine mammals including: ika-moana (large whales) of Aotearoa, New Zealand; dhangal (dugongs) of Torres Strait between northern Australia and Papua New Guinea; river dolphins and manatees of Amazonia; beluga whales, Atlantic walrus, bearded seals and harp seals of the Nunavik region of north Quebec in the Canadian Arctic; and sea otters, spotted or larga seals, northern fur seals and Steller sea lions of the Commander Islands, Russia. These accounts illustrate the complexity and temporal dynamism of indigenous knowledge. To help identify the themes in these accounts, we used an extension of Houde's typology (in Ecol Soc 12(2):34, 2007 ) of indigenous knowledge, which we envisaged as a hexagon with worldview at the core and cosmology, factual observations, management systems, past and present uses, ethics and values, and culture and identity on its faces. We hope that this chapter will help marine mammal scientists who work in research partnerships with indigenous peoples, to build trust, respect, and mutual understanding of each other’s knowledge systems. This understanding should help marine mammal scientists to work successfully across the “cultural interface to achieve true progress in marine mammal conservation and coexistence.”
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| 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".