Northern exposure: a comparison study of Alaska and Yukon models of measuring community wellbeing
Bibliographic record
Abstract
The main objective of this study is to examine models of measuring community wellbeing in Alaska and Yukon to determine if they were developed with the input of residents and if these models reflect local living conditions. Research suggests communities that establish an agreed upon model of measuring community wellbeing will benefit by having an increase in public involvement in local decision-making, and larger capture of material wealth and empowerment over resource management (Varghese et al. 2006). A core problem is that while many communities have started to develop ways to evaluate wellbeing, there is a lack of research on the various models in the Arctic. There are several unique challenges to developing a model in Arctic communities such as the clash between mainstream and Indigenous definitions of wellbeing, the lack of data and small population sizes (Taylor 2008 & Bobbitt et al. 2005). \nFor this study I conducted an in-depth search for publically available models in Alaska and Yukon and conducted semi-structured interviews with experts. Part one of the analysis was searching through records of each model to document community outreach methods, part two was an experimental content analysis to identify themes across models in both regions, and part three was a content analysis of the interviews. \nI did not find any significant difference in the design frame, content or consultation with local residents between the models in Alaska and Yukon.
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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".