Another Low Road to Basic Income? Mapping a Pragmatic Model for Adopting a Basic Income in Canada
Bibliographic record
Abstract
Abstract Drawing from both theoretical and empirical research, the literature on basic income (BI) is now voluminous, pronouncing both its merits and its limitations. Burgeoning research documents the impacts of un/conditional cash transfers and negative income tax programs, with many studies highlighting the effectiveness of these programs in reducing poverty, and improving a host of social, economic and health outcomes. We consider possible avenues for BI architecture to be adopted within Canada’s existing constellation of income security programs, to the benefit of disadvantaged groups in society. Identifying key federal and provincial (i.e., Ontario) transfer and tax benefit programs, we highlight which programs might best be maintained or converted to a BI. While opponents decry the (alleged) exorbitant costs of BI schemes, we suggest that the existing approach not only produces an ineffective system—which actually engenders poverty and the health and social problems that accompany it—but an excessively costly one.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".