Identifying indigenous knowledge components for <i>Whudzih</i> (Caribou) recovery planning
Bibliographic record
Abstract
In Canada, recent advances towards reconciliation have introduced new collaborations between Indigenous and non-Indigenous governments, including for species-at-risk recovery planning. During these collaborations, Indigenous Knowledge (IK) is often requested, however, clear expectations of what IK is being sought and how diverse knowledge systems will be woven to produce tangible benefits to species recovery are often limited. Here, we provide a case study of a two-stage process to identify and collect IK components that can aid whudzih (caribou) recovery planning. First, we surveyed non-Indigenous government professionals involved in caribou initiatives to specify what IK would benefit recovery planning. Responses were used to guide the development of semi-structured interview questions. Interviews were conducted with knowledge holders from Lhtako Dene, a Southern Dakelh Nation in British Columbia, Canada with historic socioecological ties to caribou. Responses of government professionals highlighted 24 topics for caribou recovery, and interviews with Lhtako Dene knowledge holders revealed strong linkages between ecological and social information types. In some cases, the IK requested was not available from knowledge holders. Collaborations for caribou recovery would benefit from clarity on expectations and outcomes of IK sharing. We suggest that structured processes that respectfully facilitate IK requests and collection become commonplace in species recovery planning.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".