5. Present and Future of Gender in Impact Assessment: a Standpoint—a Paradigm Shift?
Bibliographic record
Abstract
Present and Future of Gender in ImpactAssessment: a Standpoint-a Paradigm Shift?It is said that "theories are to serve a purpose of change, or none." 1 Accordingly, the overall paradigm of my study is feminist: I propose that an implementation of gender equality tools for policy IA will eventually contribute to better equality policy and programming outcomes.Mainstreaming GIA/GBA practices in public policy and programme analysis is indispensable both to fulfilling constitutional and international commitments to legal equality as well as to exercising democratic stewardship.In the first part of this last chapter, I synthesise my empirical findings, providing comparative conclusions from the Canadian and European approaches to gender analysis.In the second part, I then contextualize these empirical results in relation to feminist, post-positivist, standpoint and critical governance theories.In the third and concluding part, I present a vision for the future of IA and the role of gender analysis could play in it.2 gendeR eQuaLity goveRnance thRough iMpact assessMent: coMpaRative concLusionsThis section provides a comparative overview of the implementation and practice of gender analysis tools in the Canadian and European environments.It identifies the factors that hinder enhanced tool implementation and practice and those that drive change by providing institutional learning opportunities.The following table is a reminder of the genealogy of each tool. 1 | Bogason 2005, 251. 2 | For usage of central terminology, see subsections 1.4.1 and 1.6.24 | Just to name a few in the context of the marginalisation of gender in the realm of policy making and feminist critical governance (Brodie 1995; Carney 2004; Abu-Laban 2008;
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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".