Assessing human well‐being constructs with environmental and equity aspects: A review of the landscape
Bibliographic record
Abstract
Abstract Decades of theory and scholarship on the concept of human well‐being have informed a proliferation of approaches to assess well‐being and support public policy aimed at sustainability and improving quality of life. Human well‐being is multidimensional, and well‐being emerges when the dimensions and interrelationships interact as a system. In this paper, we illuminate two crucial components of well‐being that are often excluded from policy because of their relative difficulty to measure and manage: equity and interrelationships between humans and the environment. We use a mixed‐methods approach to review and summarize progress to date in developing well‐being constructs (including frameworks and methods) that address these two components. Well‐being frameworks that do not consider the environment, or interrelationships between people and their environment, are not truly measuring well‐being in all its dimensions. Use of equity lenses to assess well‐being frameworks aligns with increasing efforts to more holistically characterize well‐being and to guide sustainability management in ethical and equitable ways. Based on the findings of our review, we identify several pathways forward for the development and implementation of well‐being frameworks that can inform efforts to leverage well‐being for public policy.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".