Examining Distinctive Indicator Scopes Measuring Well-Being in Sustainable Development Assessment
Bibliographic record
Abstract
The significance of acknowledging well-being (WB) has increased in local sustainable development (SD) assessment. Meanwhile, scholars and practitioners have paid growing attention to using subjective indicators which rely on a person’s subjective evaluation to measure SD subjects, due to the frequent critique. The predominant use of objective indicators to assess SD frequently overlooks capturing individual’s and community’s WB. Nevertheless, the scopes and functions of subjective indicators remain underexamined in the SD assessment context. Therefore, this study discusses the distinctive characteristics of subjective sustainable development indicators (SDIs), contrasting with objective SDIs, complemented by examining WB indicators. To this end, an analysis of the literature on indicator-based assessment of SD and WB at the community and local level was conducted. The findings highlighted that the three distinctive approaches of SDIs could optimally capture and address associated WB: the objective SDIs could most sufficiently capture and address material WB capture, which turned, however, the shortcoming that overlooks other dimensions of WB. In contrast, the expert-led subjective SDIs could optimally capture and address community’s social WB, whereby the outcomes reflected social norms and preferences recognised by a community and sustainability theories. Likewise, the citizen-based subjective SDIs distinctly measured individual’s life satisfaction levels, whereby the outcomes explicitly presented individual’s subjective WB while addressing local needs and values. This study finally suggests that the complementary use of the respective SDIs contributes to a thorough local-level SD assessment, by optimally addressing associated WB, which ultimately helps meet the current and future generations’ WB in achieving local SD.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".