Achieving equity in housing: a comparison of gender-based analysis frameworks in housing policies in Canada and France
Bibliographic record
Abstract
National housing strategies have been put into place to improve housing accessibility and quality. Canada is new to this, publishing its strategy in 2017 and act in 2019 recognising housing as a human right. The Canadian strategy features GBA+ (gender-based analysis plus intersectionality) frameworks to guide equitable housing policy development. However, how GBA+ frameworks are implemented in practice and measured for equitable outcomes remains unclear and in need of investigation. French policy has a GM (gender mainstreaming) approach in its national housing strategy (2017). France has recognised the right to housing since 1995 and considered housing an opposable right in law in 2007. Thus, we investigate how GM and GBA+ are conceptualised, developed and implemented in Canadian (short term) and French (in the longer-term) housing strategies and to what effect (in France) through content and critical discourse analyses of policy documents. Findings lead to policy recommendations which include the need to focus on multijurisdictional housing policy implementation plans which work within the tensions of a legal framework created to implement a universal right to housing and the narrow particularism of regional implementation, as well as a reconceptualization of how GBA+ is rolled out in Canadian housing policy delivery.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.020 | 0.020 |
| Scholarly communication | 0.016 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".