Forging a new path for multi-cultural fishery management
Bibliographic record
Abstract
In 2007, the state of Michigan and five tribes entered into a consent decree that provides for collaborative fishery management. The parties desire a productive co-management relationship and realize the need to understand perspectives towards fishery management. State and tribal participants were interviewed, with some tribal agency participants being non-tribal member employees. The findings revealed a lack of understanding between the groups for management priorities and a wide cultural distance between backgrounds in either the Indigenous and/or Western knowledge systems. State employees described their qualifications through Western education and individual learning while tribal member employees identified theirs as community experiences and relations with the natural world. The responsibility to manage was described through the public trust doctrine by state participants, whereas tribal participants described sacred responsibilities, and protecting treaty rights and culture. The purpose of fishery management as described by state participants was through sustainability of the resource and harvest while tribal participants spoke of balance within the Circle of Life concept and suggested the term ‘management’ was inappropriate. The tribal agency employees who were non-tribal members, used both knowledge types expressing scientific concepts while using Indigenous expressions learned partially through their normative presence in tribal communities. Opportunities exist for dismantling cultural distance, forming a collective identity, and sharing power, if state governance structures adapt to include Indigenous knowledge and practices. This may require restructuring governance arrangements to promote power sharing and the normative presence of cross-trained employees within the respective communities where structured learning exist and cultural sharing begins.
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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 | 0.014 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.029 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.002 | 0.022 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".