Is It What You Measure That Really Matters? The Struggle to Move beyond GDP in Canada
Bibliographic record
Abstract
In light of Gross Domestic Product’s (GDP) well-known limitations as a wellbeing indicator, many alternative measures have been developed around the world. Some advocates of “beyond GDP” measures argue that they are key to shifting societal priorities away from economic growth toward sustainability, equity, and well-being. Is there any evidence to date that alternative indicators have lived up to their supporters’ expectations, whether the hope is for a radical transformation of social priorities away from GDP growth or a reformist vision of better policymaking without challenging the growth paradigm? What are the obstacles to fulfilling those expectations? This article examines the Canadian experience, drawing on interviews with researchers, non-governmental organization (NGO) leaders, public-sector officials, and politicians, along with analysis of relevant documents. The hopes of Canadian proponents of new wellbeing measures have been largely disappointed to date, as no impact on federal or provincial policy is evident. Obstacles facing both a transformative and more limited reformist vision are examined. The Canadian case also suggests that use of new socio-economic indicators is best seen as one product of political efforts to bring ecological and social values into decision-making, rather than as the transformative force that will cause a change in societal priorities.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.034 | 0.017 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 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".