An Analysis of 2023-2024 Survey Data for the Northern Connections Project in Alaska and Northwestern Canada
Bibliographic record
Abstract
This report evaluates a 2023–2024 survey of community-based monitoring (CBM) programs across Alaska and northwestern Canada to inform the Northern Connections project’s regional collaboration strategy. The findings highlight a critical vulnerability in CBM stability, as most initiatives rely on single-source federal funding and require support to diversify their funding portfolios. While local participation and Indigenous Knowledge are robust during data collection, a significant gap exists at the interpretation and analysis stages; consequently, the report recommends that CBM programs and supporting entities prioritize technical training and capacity-building to center Indigenous knowledge in data interpretation. Furthermore, despite widespread interest in cooperation, a persistent disconnect remains across the Alaska-Canada border. To bridge this gap, the report suggests establishing regional monitoring networks organized around broad, cross-cutting issues—such as food security and climate change—rather than narrow, topic-specific silos. These issue-based networks offer the greatest potential for participation and operational synergy, providing a framework for shared data management, policy alignment, and expertise exchange. Ultimately, the report concludes that while the groundwork for regional coordination is well-established, Northern Connections and similar efforts need to prioritize international networking and issue-led collaboration to maximize the collective impact of CBM on Arctic policy and resource management.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".