Nunavik Sentinels: documenting northern insect biodiversity and supporting Indigenous youth leadership through participatory research and education
Bibliographic record
Abstract
Abstract There is strong evidence that a biodiversity crisis is underway, fuelled by pollution, climate change, and invasive species. These impacts are especially important in northern Canadian regions. However, insect and other arthropod monitoring in Northern Canada urgently needed for land preservation is lacking. This paper presents the Nunavik Sentinels, a community-based participatory research programme (see definition, Table 1), that is aiming to fill those knowledge gaps while promoting collaboration among all stakeholders (organisations and members of northern communities, and scientists). Nunavik Sentinels is a unique insect-monitoring programme facilitated by the Montréal Insectarium – Espace pour la vie (Montréal, Québec, Canada), making entomology accessible to Indigenous youth by providing them with tools to lead expeditions in unexplored habitats and involving them in data collection. We present how this programme came to existence and its four-pillar framework ( i.e. , training land camp, summer employment, educational kit, and research). We touch upon how the programme is continually evolving. Finally, we demonstrate the benefits of the programme and how it will help better define the actions to be taken to prepare for future changes in northern biodiversity.
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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 | 0.013 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".