Rethinking What Counts. Perspectives on Wellbeing and Genuine Progress Indicator Metrics from a Canadian Viewpoint
Bibliographic record
Abstract
A prevailing undercurrent of doubt regarding the merits of economic growth has motivated efforts to rethink how we measure the success of economic policy and societal wellbeing. This article comments on efforts to better account for impacts of economic activity emphasizing genuine progress indicator (GPI) and wellbeing metrics from a Canadian viewpoint. The authors caution that GPI and related metrics are measures of human and social welfare and not adequate to account for the ecological costs associated with economic growth. In addition, the article discusses the suitability of wellbeing models and metrics for local scale applications, recognizing growing interest in these techniques at the urban and local level. The article concludes with a reflection on the uptake of GPI and wellbeing measures highlighting the Canadian experience.
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 | 0.023 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.014 | 0.060 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 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".