Resource Management for the Next Generation : Co-Management of Fishery Resources in the Western Canadian Arctic Region
Bibliographic record
Abstract
Inuvialuit in the Western Canadian Arctic region have maintained a tradition of hunting and fishing, ofharvesting wildlife fbr their daily food. Since 1984 when the Inpvialuit Final Agreement was signed, they have been managing Iocal renewahle resources2) in cooperation with the Government ofthe Nonhwest 'Ibnitories and the Canadian federal government. RecentlM co-management systems in which al)original people and both levels ofgovemment work together to manage resources has become accepted as an alternative to governnient-centered management systems. Inuvialuit have practiced co-management fbr almost twenty years and their case has been viewed as one ofthe most successfu1. Furthermore, they have been active in sharing their experiences with other aboriginal peoples and government agents. Many researchers have discussed the problems associated with resource management systems in which governments and the i'nternational organizations play a central role [PiNKERToN 1989; DEsoMBRE 2001; IwAsAKi-GooDMAN 2002] and various attempts have been made to shift the responsibility of resource management from the central government to the local resource users. Consequently,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".