Bibliographic record
Abstract
Arctic Social Indicators II (ASI-II) is a follow-up activity to ASI-I (2010) and the first Arctic Human Development Report (AHDR, 2004). The objective of ASI (2010) was to develop a small set of Arctic specific social indicators that as a collective would help facilitate the tracking and monitoring of change in human development in the Arctic. ASI indicators were developed for six domains that are considered prominent aspects of human development in the Arctic by residents in the Arctic: Health and Population; Material Wellbeing; Education; Cultural Wellbeing; Contact with Nature; and Fate Control. The objective of the present volume of ASI is to present and discuss the findings of the work on measuring the set of recommended ASI indicators; to conduct a series of regional case studies to illustrate and test the strength and applicability of these indicators; to identify and describe data challenges for the Arctic region specifically in relation to these Arctic specific indicators and to draw conclusions about the ability of ASI to track changes in human development; and to formulate policy relevant conclusions for the long-term monitoring of Arctic human development. The core content of ASI-II is a set of five carefully selected case studies, which form the basis for drawing conclusions about the applicability of the ASI indicators and for formulating policy relevant conclusions. Case studies are performed for Sakha Republic (Yakutia); the West-Nordic Region; Northwest Territories; Inuit Regions of Alaska; and the Inuit World, with the Survey of Living Conditions in the Arctic (SLiCA) used to augment ASI. Findings on the state and changes in Arctic human development and wellbeing are presented. Based on our analysis and conclusions from the five case studies the framework for an ASI monitoring system is introduced. We argue that the long-term monitoring of human development in the Arctic would be greatly facilitated by the regular and frequent collection and reporting of relevant data, including those required for the proposed small set of ASI indicators.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.022 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.038 |
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".