Exploring Nation-specific solution options for the Tla'amin Nation's seafood security under changing climate conditions
Bibliographic record
Abstract
Climate change is altering marine ecosystems while interacting with existing socioeconomic barriers to shape Indigenous seafood security. Coastal First Nations in British Columbia face climate-driven declines in culturally important species such as salmon, eulachon, herring, and butter clams, along with long-standing challenges including limited disposable income, uneven access to vessels and gear, and restricted local authority over marine resources. In collaboration with the Tla’amin Nation, this research aims to develop a social-ecological, trait-based framework that connects human-nature and decision-support traits, socioeconomic barriers, culturally important species, and community-defined adaptation initiatives (“seeds”). Drawing on literature and Tla’amin-led processes, the framework redefines seafood security as relationships among people, species, and institutions rather than simply harvest volumes. A mixed-methods design is used to operationalize the social-ecological trait-based framework, integrating semi-structured expert interviews, community workshops, and species projections from a Dynamic Bioclimate Envelope Model. A fuzzy-logic algorithm is used to quantify the strength with which each of twelve selected “seeds” supports key traits, overcomes barriers, mobilizes local and resilient species, and contributes to climate resilience, while a climate factor derived from species-level projections captures each “seed’s” sensitivity to future ocean conditions. Results show that a small subset of five “seeds”, including a marine spatial plan, traditional food processing facility, seafood gardens, traditional ecological knowledge and skills workshops, and employing super harvesters, are especially influential in sustaining equitable access to local seafood in Tla’amin. Although climate change is expected to deepen existing inequities by further affecting vulnerable species, emerging species such as albacore tuna and sea cucumbers are predicted to become more available, helping to offset these marine food losses. A social-ecological trait-based framework functions as a transparent, adaptable decision-support tool for the Tla’amin Nation and offers a transferable template for other coastal First Nations seeking to evaluate and prioritize Nation-specific “seeds” that support local seafood sovereignty and climate resilience.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".