Developing socio-ecological indicators for changing Northern Coastal environments
Bibliographic record
Abstract
Monitoring tools and indicators that incorporate ecological and socio-economic aspects of ecosystems can lead to improved management outcomes and resource use benefits. Local and Indigenous communities in Northern coastal environments, including Nunatsiavut (northern Labrador, Canada), strongly rely on marine resources for food security, social, economic, and cultural integrity. Integrating Indigenous Knowledge and Western science through ethical and principled collaboration with local stakeholders and rights holders is a prerequisite for improving outcomes that support the priorities of local communities. Here, we identify a framework for developing socio-ecological indicators for northern coastal systems using case studies from our research program in Nunatsiavut. We highlight the importance and challenges of integrating science and local knowledge for ocean monitoring and management, and share our experiences to guide future efforts. Our 5-year collaborative research program identifies indicators of status and function of coastal ecosystems, moving beyond historical Western science practices by incorporating local and regional socio-cultural knowledge and needs. We propose that monitoring programs should include practical and accessible indicators that support Inuit priorities (e.g., ice thickness, fish size, and fish flesh color) that local communities and resource users can sustainably monitor and link to local priorities.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".