Bibliographic record
Abstract
There is renewed discussion of a basic or guaranteed income at both the federal and the provincial levels in Canada, but counterarguments about the cost, work disincentives, and electoral appeal of such schemes remain challenging. In this article, we argue that a grand plan for a basic or guaranteed income is unnecessary because self-financing redesign of existing tax credits to be refundable can better target benefits to low-income families while improving tax equity. Using 2015 tax and transfer parameters and estimates of income and population, we assess the federal transfer system as a source of universal income security, identify the revenues that can be raised through the elimination of selected federal tax credits, present four options that could be financed within that budget constraint, assess their performance, and select our preferred universal basic guaranteed income (UGBI) option. We then provide a more detailed assessment of the impact of our preferred UGBI design and discuss the extension of that design to provincial tax and transfer systems. We estimate that the combined federal and provincial UGBI that we propose would effectively target benefits to low-income households and virtually eliminate poverty for all but single non-elderly individuals at a modest efficiency cost in terms of work disincentives.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".