Indigenous food harvesting as social–ecological monitoring: A case study with the Gitga'at First Nation
Bibliographic record
Abstract
Abstract Indigenous peoples have been monitoring and managing the natural resources in their homelands and waters for millennia. Meanwhile, social–ecological systems thinkers are embracing the capacity of Indigenous knowledge systems, which are informed by land‐based practices, to inform adaptive management. Following the collaborative design of a community‐based social–ecological monitoring system over two traditional seafood harvesting seasons, we conducted a conceptual framework analysis of meeting notes and interview transcripts with Gitga'at harvesters and knowledge holders to discern how Gitga'at people monitor their territory and what indicators they focus on. An interconnected set of social–ecological concepts and indicators emerged, evidencing an intrinsic part of Gitga'at life: Gitga'at harvesters closely monitor their coastal social–ecological system through ongoing land‐ and sea‐based practices. The conceptual framework highlights the importance of maintaining and revitalizing Indigenous knowledge and harvesting practices to inform social–ecological monitoring and adaptive management at local and broader scales. Amidst discussions of marine and coastal resource co‐management in British Columbia, our results also suggest opportunities for scientific approaches to situate themselves within and support existing Indigenous frameworks and priorities. This research also adds to the discussion on the development of appropriate regional and global indicators and frameworks to monitor the resilience of social–ecological systems. A free Plain Language Summary can be found within the Supporting Information of this article.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.025 | 0.009 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".