Advancing gender budgeting in Canada
Bibliographic record
Abstract
This Chapter assesses the efforts of the Government of Canada so far in relation to gender budgeting and provides recommendations for how developments to date can be built upon to ensure a more effective and sustainable approach. Gender budgeting has only recently been introduced in Canada but is already proving itself to be an influential tool, encouraging departments to think in a more structured way about the design and conduct of GBA+ and to develop policies that help achieve gender results. Significant gender budgeting attention has focussed on the introduction of gender equality-related content in the budget. However, the presentation of this information is just one element of the wide-ranging approach to gender budgeting being developed by Canada. Reviewing the gender impact of baseline spend and strengthening the application of a gender lens to ex post processes such as evaluation and spending review will help ensure that gender budgeting is embedded across the full budget cycle. Parliamentary scrutiny of gender budgeting remains at an under-developed stage, but will be essential to ensure the government is held to account for its actions in this area.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".