Aligning Public Health With a Well-Being Economy: Opportunities and Challenges in Addressing Root Causes of Health Inequities; Comment on "Can a Well-Being Economy Save Us?"
Bibliographic record
Abstract
Labonté offers important critical optimism around the idea of a well-being economy, which is gaining considerable international momentum and offers a much-needed alternative to the current political economic paradigm of neoliberal capitalism and its significant social and ecological consequences. Because of its focus on systems and structures that constitute "root causes" of poor health and health inequities at the population level, a well-being economy aligns strongly with stated tenets and value commitments of public health. It thus provides an important opportunity for public health communities to engage and mobilize as a collective around this important vision. For this to happen, however, public health communities must overcome a reluctance to engage with political economy and take seriously the field's commitment to the public's health. In this commentary I reflect on these opportunities and challenges in the Canadian public health context.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.022 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.011 | 0.005 |
| Research integrity | 0.070 | 0.059 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".