A universal basic income: the answer to poverty, insecurity, and health inequality?
Bibliographic record
Abstract
Early evidence suggests substantial health dividends For four years in the mid-1970s an unusual experiment took place in the small Canadian town of Dauphin. Statistically significant benefits for those who took part included fewer physician contacts related to mental health and fewer hospital admissions for “accident and injury.” Mental health diagnoses in Dauphin also fell. Once the experiment ended, these public health benefits evaporated.1 What was the treatment being tested? It was what has become known as a basic income—a regular, unconditional payment made to each and every citizen. This ground breaking experiment, an early randomised trial in the social policy sphere, ran out of money before full statistical analysis after a loss of political interest. The link between inequality and poor health outcomes is long established.2 The actual mechanisms behind that link are less understood. The data from the Dauphin study, re-examined by a team from the University of Manitoba in the 2000s, suggest …
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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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.019 | 0.029 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".