Indicators of food and water security in an Arctic Health context – results from an international workshop discussion
Bibliographic record
Abstract
In August 2012, a literature search with the aim of describing indicators on food and water security in an Arctic health context was initialized in collaboration between the Arctic Human Health Expert Group, SDWG/AHHEG and the AMAP (Arctic Monitoring and Assessment Programme within the Arctic Council) Human Health Assessment Group, AMAP/HHAG. In December 2012, workshop discussions were performed with representatives from both of these organizations, including 7 Arctic countries. The aim of this article is to describe the workshop discussions and the rational for the 12 indicators selected and the 9 rejected and to discuss the potential feasibility of these. Advantages and disadvantages of candidate indicators were listed. Informative value and costs for collecting were estimated separately on a 3-level scale: low, medium and high. Based on these reviews, the final selection of promoted and rejected indicators was performed and summarized in tables. Among 10 suggested indicators of food security, 6 were promoted: healthy weight, traditional food proportion in diet, monetary food costs, non-monetary food accessibility, food-borne diseases and food-related contaminants. Four were rejected: per-person dietary energy supply, food security modules, self-estimated food safety and healthy eating. Among 10 suggested indicators of water security, 6 were promoted: per-capita renewable water, accessibility of running water, waterborne diseases, drinking-water-related contaminants, authorized water quality assurance and water safety plans. Four were rejected: water consumption, types of water sources, periodic water shortages and household water costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".