Gender justice and the Millennium Development Goals: Canada and South Africa considered
Bibliographic record
Abstract
The expiration of the Millennium Development Goals (MDGs) in 2015 means that the global development industry has been working towards crafting an MDG 2.0, for the post-2015 context: the Sustainable Development Goals. There is both an explicit and an implicit gender focus in both the MDGs and SDGs. Three of the eight MDGs were directly targeted at women and girls, while there was a stated understanding that all eight goals apply equally to men and women. Despite robust PR, the 2010 and 2012 United Nations (UN) Reports on the MDGs cited persistent violence against women, and continued discrimination in access to education, work, and participation in governance. In other words, the MDGs and their metrics appear to have reaffirmed existing gender norms and relations rather than challenging the status quo. We survey critical explanations of this failure, in the Canadian and South African contexts, and apply a critical feminist lens to suggest an alternative framing of gender justice. We suggest that one cannot understand the limitations of the MDG model of empowerment for women without considering the broader socio-political and ideological contexts that shape developmental interventions: an understanding that is critical to the design of the SDGs if they are to transcend the shortcomings of the MDGs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.018 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".