A Call for Urgent Monitoring of Food and Water Security Based on Relevant Indicators for the Arctic
Bibliographic record
Abstract
This perspective paper argues for an urgent need to monitor a set of 12 concrete, measurable indicators of food and water security in the Arctic over time. Such a quantitative indicator approach may be viewed as representing a reductionist rather than a holistic perspective, but is nevertheless necessary for actually knowing what reality aspects to monitor in order to accurately understand, quantify, and be able to project critical changes to food and water security of both indigenous and non-indigenous people in the Arctic. More relevant indicators may be developed in the future, taking us further toward reconciliation between reductionist and holistic approaches to change assessment and understanding. However, the potential of such further development to improved holistic change assessment is not an argument not to urgently start to monitor and quantify the changes in food and water security indicators that are immediately available and adequate for the Arctic context.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.009 |
| 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".