Linking co-monitoring to co-management: bringing together local, traditional, and scientific knowledge in a wildlife status assessment framework
Bibliographic record
Abstract
Effective wildlife management requires accurate and timely information on conservation status and trends, and knowledge of the factors driving population change. Reliable monitoring of wildlife population health, including disease, body condition, and population trends and demographics, is central to achieving this, but conventional scientific monitoring alone is often not sufficient. Combining different approaches and knowledge types can provide a more holistic understanding than conventional science alone and can bridge gaps in scientific monitoring in remote and sparsely populated areas. Inclusion of traditional ecological knowledge (TEK) is core to the wildlife co-management mandate of the Canadian territories and is usually included through consultation and engagement processes. We propose a status assessment framework that provides a systematic and transparent approach to including TEK, as well as local ecological knowledge (LEK), in the design, implementation, and interpretation of wildlife conservation status assessments. Drawing on a community-based monitoring program for muskoxen and caribou in northern Canada, we describe how scientific knowledge and TEK/LEK, documented through conventional monitoring, hunter-based sampling, or qualitative methods, can be brought together to inform indicators of wildlife health within our proposed assessment framework. Atuttiaqtut angutikhat aulatauni piyalgit nalaumayumik piyarakittumiklu tuhagakhat nunguttailininut qanuritni pitquhitlu, ilihimanilu pityutit pipkaqni amigaitnit alanguqni. Naahuriyaulat munarini angutikhat amigaitni aaniaqtailini, ilautitlugit aaniarutit, timai qanuritnit, amigaitnitlu pitquhit hiamaumanilu, atugauniqhauyut pitaqninut una, kihimik atuqtauvaktut naunaiyaiyit munariyauni kihimik amihuni naamangitmata. Ilaliutyaqni allatqit pityuhit ilihimanitlu qanuritni piqarutaulat tamatkiumaniqhanik kangiqhimani atuqtauvaktuniunganit naunaiyaiyit munarinit ahiniittut akuttuyunik amigaitni inait. Ilaliutyaqni pitquhit uumatyutit ilihimani (TEK) qitqanittut angutikhat aulaqataunit havariyaqaqtai tapkuat Kanatamiuni nunatagauyut ilaliutivakniqhatlu atuqhugit uqaqatigikni piqataunilu pityuhiit. Uuktutigiyavut qanuritnia naunaiyaqni havagut piqaqtitiyuq havagutikhainik hatqiumanilu pityuhit ilautitlugit Pitquhit Uumatyutit Ilihimanit (TEK), tapkualuttauq nunalikni uumatyutit ilihimanit (LEK), hanatyuhikhaini, atuqpaliani, tukiliuqnilu angutikhat nunguttailini qanuritnit naunaiyaqni. Pivigiplugit nunaliuyuningaqtut munaqhityutit havagutit umingmaknut tuktutlu ukiuqtaqtuani Kanata, unnirtuqtavut qanuq naunaiyaiyit ilihimani tapkuatlu TEK/LEK, titiqhimani atuqhugit atuqtauvaktut munaqhityutaunit, angunahuaqtumingaqtut naunaiyagat, uvaluniit nakuuninut pityuhit, atauttimuktaulat tuhaqhitninut naunaipkutat angutikhat tahamani uuktutauyuq naunaiyaqni havagutai.
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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 | 0.106 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.017 | 0.007 |
| Science and technology studies | 0.012 | 0.045 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.007 | 0.025 |
| Research integrity | 0.006 | 0.006 |
| 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".