Indigenous community preferences for food and ceremonial fishery outcomes: Quantifying the importance of harvestable biomass and spatial distribution via a discrete choice experiment
Bibliographic record
Abstract
Fisheries are inherently complex, with important interactions among biological dynamics, the environment, and the socio-economic systems in which they are embedded.Managing fisheries for both short-and long-term sustainability requires taking a management-oriented paradigm focused on meeting goals and objectives that are important and acceptable to all fisheries participants.Indigenous communities regularly feel that they are under-represented in fisheries decision-making, and that their cultural and livelihood objectives are ignored.Governments want to integrate Indigenous criteria into their definition of fisheries management success, but to date there is a lack of tools and processes to help Indigenous communities quantify their objectives in a way that can effectively inform the DFO process.Using a case study on the West Coast of Vancouver Island (WCVI), this project examines how a simple survey with a discrete choice experiment (DCE) can be used to help quantify Indigenous objectives.I worked with the Nuu-chah-nulth Indigenous community to design and implement a DCE to determine their preferences for the outcomes of a food and ceremonial fishery.The DCE provided quantitative information to show positive preferences for increased layers of spawn on bough and quality of spawning area, and negative preferences for increasing number of spawning areas and increasing travel time.Additionally, we found evidence of a shifting preference baseline in the Nuu-chah-nulth community, highlighting a loss of traditional Nuu-chah-nulth knowledge caused by low herring abundances along the WCVI.DCE results are supported by qualitative comments from the Nuu-chah-nulth community, making us confident that the DCE was able to effectively represent community preferences.Overall, we found that DCE's can help Indigenous communities translate their general fishery goals into specific measureable objectives, allowing their goals and values to be better represented and included in fisheries management decision-making.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".