Views of traditional ecological knowledge in co-management bodies in Nunavik, Quebec
Bibliographic record
Abstract
Although there is increasing recognition that traditional ecological knowledge can make important contributions to environmental and resource-management issues, there are also indications that its use in co-management committees has not been straightforward. Three main sets of challenges have been documented — differences in knowledge systems between western scientific and traditional ecological knowledge, the relatively powerful position of western science and scientists in comparison to traditional ecological knowledge and its users, and challenges in documenting and presenting traditional ecological knowledge. This paper reports the results of a study that surveyed members of co-management committees established in Nunavik, northern Quebec, pursuant to the James Bay and Northern Quebec Agreement to explore their perspectives on these issues. Three elements emerged from this study. They are the complex and sometimes contradictory nature of the views that committee members held about traditional ecological knowledge, the active role of the Inuit in attempting to shape how traditional ecological knowledge is used in decision-making, and the need for documentation of, and research funding for, the collection of traditional ecological knowledge.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".