What's My METR? Marginal Effective Tax Rates Are Down - But Not for Everyone: The Ontario Case
Bibliographic record
Abstract
The marginal effective tax rate (METR) on personal income, explain the authors, measures the impact, on take-home pay, of federal and provincial income taxes combined with the impact of reductions and clawbacks of income-tested tax credits and benefits as individual or family income rises. These income-tested credits and benefits mostly target financial support to low- and middle-income families with children, or to low-income seniors. As their income rises past prescribed thresholds, clawbacks and reductions begin, raising the METR on each dollar of incremental income above the threshold. Policymakers interested in keeping down METRs overall, say the authors, should consider reinvigorating the personal income tax relief imperative, rather than implementing or expanding targeted benefits that make general tax relief more difficult to achieve.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".