Grizzly and polar bears as nonconsumptive cultural keystone species
Bibliographic record
Abstract
Grizzly bears and polar bears often serve as ecological “flagship species” in conservation efforts, but although consumptively used in some areas and cultures they can also be important cultural keystone species even where not hunted. We extend the application of established criteria for defining cultural keystone species to also encompass species with which cultures have a primarily nonconsumptive relationship but that are nonetheless disproportionately important to well-being and identity. Grizzly bears in coastal British Columbia are closely linked to many Indigenous Peoples (including the Haíɫzaqv (Heiltsuk), Kitasoo/Xai’xais, and Nuxalk First Nations), where they are central to the identity, culture, and livelihoods of individuals, families, Chiefs, and Nations. Polar bears in Churchill, Manitoba, provide another example as a cultural keystone species for a mixed Indigenous and non-Indigenous community in which many of the livelihood benefits from the species are mediated by economic transactions in a globalized tourism market. We discuss context specificity and questions of equity in sharing of benefits from cultural keystone species. Our expanded definition of cultural keystone species gives broader recognition of the beyond-ecological importance of these species to Indigenous Peoples, which highlights the societal and ecological importance of Indigenous sovereignty and could facilitate the increased cross-cultural understanding critical to reconciliation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".