Gender Based Analysis (GBA) in Canada: What Can Sectoral Ministries do in India?
Bibliographic record
Abstract
Prima facie evidence may suggest that Canadian experience of GBA contribute little to the process of gender budgeting in India as Finance Canada is outside the purview of GBA. But the point to be noted is that Canadian experience of GBA across Federal Departments throws light to a significant institutional networking system to establish gender budgeting across identified Departments in India from the limited purview of macro-scan of Union Budgets. At the same time, as Indian experience revolves around gender analysis of budgets and Canadian experience is more comprehensive across various Federal Departments but excluding the engendering of budgeting process in Finance Canada, the learning process is definitely symbiotic. This symbiotic process can ensure a two-way learning process between India and Canada in the realm of gender mainstreaming. Canada can draw lessons from India on integrating gender perspective in budgetary policies and India can draw lessons from Canada on the strategies and tools of mainstreaming gender across various Ministries/Departments.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.039 | 0.016 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".