Canada considers a basic income guarantee: can it achieve health for all?
Bibliographic record
Abstract
There is little doubt that the implementation of a Basic Income Guarantee (BIG) in Canada and other liberal welfare states would alleviate some of the most egregious examples of absolute poverty that contribute to poor health such as lack of adequate food and shelter and inability to meet basic household and personal needs. BIG would likely improve the health of the most disadvantaged by moving them closer to the relative poverty line. Yet, advocacy for and implementation of BIG carries potential dangers. Since health improves with every step up the income ladder, simply moving people closer to the relative poverty line without providing additional universal benefits and supports common to most other developed nations would limit its health promotion potential. In addition, governing authorities in liberal political economies can use BIG to justify continuing imbalances in economic and political power that skews the distribution of the social determinants of health. In addition, implementation of BIG -- despite its more progressive advocates calls for maintaining or enhancing of existing social programs - can serve as justification for reducing or removing these programs, thereby threatening health.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 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".