Bibliographic record
Abstract
Poverty and rising income inequality in Canada have brought demands for improved government action on redistribution. Unfortunately, such pleas risk being overshadowed by a looming fiscal crunch as the baby boomers retire. An expanding population of seniors will add at least one percent annually to both growing health and OAS/GIS costs so that, absent meaningful change, other spending will have to be slashed by an average of 20.2 percent by 2032 if total spending and revenues are not to rise relative to GDP. For Canada’s tax-transfer system to keep fulfilling its redistributive role, a fundamental rethink is required. With non-seniors spending being squeezed, some changes in tax mix, moderate revenue increases and refined targeting of transfers will be needed to protect the system’s progressive nature. Increasing personal income tax and reducing property tax by an offsetting amount would improve redistribution without raising taxes. More revenue could be obtained without severe distortions via a capital transfer tax, the elimination of boutique credits aimed at niche beneficiaries, or perhaps a dual income tax which exacts more from labor than capital income. Improvements to existing transfer programs are another way forward. The conversion of EI to a purely insurance basis, freeing up funds to support redistribution via refundable credits is a possibility. Another cost-saver involves removing the indexation of the OAS/GIS income threshold and allowing its real value to decline, making more recipients subject to clawbacks. Whichever course governments pursue, revamping Canada’s taxtransfer system will be a delicate and difficult task. This paper explores the policy choices available, and makes it clear that time is not on our side.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.024 | 0.011 |
| Insufficient payload (model declined to judge) | 0.056 | 0.006 |
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".