Advancing a WEF nexus security index for Alaska: an informed starting point for policy making
Bibliographic record
Abstract
Water, energy, and food (WEF) security has become a key area of research in sustainable development. Prompted by the United Nations’ Sustainable Development Goals, a large body of research highlights the inter-connectedness of WEF systems and the dangers of their siloed treatment. Simultaneously, more attention is being placed on the WEF security of remote populations in the Arctic, especially for areas like Alaska, where WEF insecurities are prevalent, yet obscured by national reporting. This research developed a WEF Nexus Security Index for the state of Alaska that can be used by policy-makers to consider plausible causes for regional inequalities and why some regions of Alaska, and not others are exposed to higher levels of WEF insecurity. Beyond the conceptual value of the index, an on-line Arctic WEF Nexus Security portal was developed that further enables policy-makers and community leaders to track changes in WEF security and monitor the effects stemming from future interventions, be they policy or technological in nature. By bridging nexus thinking and nexus doing, we hope to provide policy-makers with a novel and accessible tool for achieving WEF security in Alaska.
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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 | 0.027 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".