Restorative diets: a methodological exploration comparing historical and contemporary salmon harvest rates
Bibliographic record
Abstract
Along the coast of what has come to be known as British Columbia, First Nations face persistent challenges related to the state of the fisheries on which they depend. Fisheries management strategies imposed by the colonial-through-to-federal governance regimes have been implicated in contributing to the challenges, and are rejected by many coastal First Nations who are reasserting governance authority over their fisheries. In particular, the current management approach continues to set ceilings on First Nations’ harvest rates (e.g., food, social, and ceremonial allocation). Too often the evidence used to determine such ceilings reflects diets and fishing practices deeply disrupted by social-ecological change, including, but not limited to, colonialism and climate change. Through this paper we use the example of salmon to propose harvest rates more consistent with less disrupted diets, what we refer to as restorative diets. Methodologically, we use empirical records on historical diets as a basis for envisioning what restorative diets might look like and for considering the magnitude of the difference between harvest rates consistent with such diets compared to contemporary diets. We do so by developing a model of restorative harvest rates in reference to caloric needs, the proportion of diets historically contributed by salmon, and the amount of salmon harvested per calorie, which we parameterize using existing empirical records. These methods yield coast-wide restorative harvest rates that range from 68 to 235 kg of salmon per person per year. Such estimates are three to 14 times higher than contemporary rates. We offer the methodology and findings presented here as both catalyst and guidance for further investigations of the conditions (ecological, social, and political) necessary to support the efforts of coastal First Nations, and Indigenous Peoples globally, to restore their fisheries, diets, and food systems.
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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 | 0.033 | 0.112 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| 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".