Policy Forum: Does Non-Filing Hinder Access to the Canada Learning Bond for Low-Income Families?
Bibliographic record
Abstract
Since the Canada learning bond (CLB) launched in 2005, 1.6 million children have ever received a CLB payment; these are from among the 3.8 million children who have ever been officially eligible. Starting in 2022, hundreds of thousands of CLB-eligible children will become adults able to make an independent claim for a retroactive benefit payment. While these adults will have formed their own core fiscal unit, eligible for other transfers tied to their own taxfiling, their access to the CLB will be determined by a core fiscal unit that they have left. This arrangement pushes at the boundaries of Canada's tax and transfer system, which generally assumes coordination within a core fiscal unit, where a fiscal unit refers to family grouping used by the Canada Revenue Agency to asses taxes and transfers. Extant data suggest that taxfiling may be an administrative obstacle for a non-trivial share of children and young adults, but it has not yet received much attention from policy makers. In this article, I discuss options for policy change that might improve access to the CLB by reducing reliance on annual filing to assess income for both adult claimants and current minors eligible for the program.
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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 | 0.007 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.006 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.017 | 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".