Constituents over Correlation: Indicators and Arctic Urban Decision-Making
Bibliographic record
Abstract
Arctic city mayors influence municipal sustainability outcomes, navigating decisions on waste management, social service funding, and economic development. How do mayors make these decisions and to what extent do they integrate sustainability indicator data? Interviews with the mayors of Fairbanks, Alaska, Yellowknife, Canada, and Luleå, Sweden, revealed indicators are used on a case-by-case basis to track trends but lack systematic integration into decision-making. Constituent concerns drive agendas rather than indicator trends. Based on International Organization for Standardization (ISO) guidelines, 128 indicators grouped into 19 sustainability themes were compiled from 2000 to 2019 for the study cities. Partial Least Squares Structural Equation Modeling (PLS-SEM) was applied to examine the utility of ISO indicators as a guiding factor for sustainability trend tracking, identifying key themes for each city. Results show that indicator trends are too inconsistent and interconnected to be useful as an independent form of guidance for mayors. For Arctic municipalities, sustainability indicator datasets are useful in specific circumstances, but they do not provide the same kind of decision-making heuristic that mayors receive from direct constituent interaction. Findings emphasize the importance of more robust data collection and the development of management frameworks that support sustainability decision-making in Arctic cities.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 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".