Transparency, citizen consultation and accountability
Bibliographic record
Abstract
This Chapter looks at the extent to which the Government of Canada is transparent and consultative in its approach to gender equality policy development. A transparent government is critical to building citizen trust and, together with citizen consultation, can underpin evidence-based gender policy-making and improved gender policy results. Canada has taken steps forward in this regard in recent years, and can strengthen this through increased transparency in relation to GBA+ and embedding more systematic and meaningful citizen consultation in the policy development process. This Chapter also considers scrutiny mechanisms in relation to the government’s actions and decisions to achieve greater gender equality. Accountability for the government’s actions on gender could be strengthened if the Parliament of Canada and the Canadian Office of the Auditor General build on their successful interventions in the area of GBA+ and incorporate a greater gender perspective in broader aspects of their work.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.017 | 0.036 |
| Scholarly communication | 0.023 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 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".