UN Women’s feminist engagement with governance by indicators in the Millennium and Sustainable Development Goals
Bibliographic record
Abstract
The rise of evidence-based policy has brought with it an increase in the use of indicators and data-driven global projects. The United Nations System has used the indicator-based Millennium Development Goals (MDGs) and Sustainable Development Goals (SDGs) projects to govern policy from above. Of particular interest in this article is how indicators are used to govern gender equality initiatives within the Goals. By using ‘governance by indicators’ as a framework for understanding global policy processes, we can better understand how the power of indicators can help or hinder progress towards gender equality depending on the extent to which it renders gendered concerns visible. Studying indicators in this forum also illuminates spaces of contestation, where policy actors can debate indicators and reshape meaning. Based on this framework, this article explores UN Women’s feminist critique of measurement and knowledge production in the MDGs and SDGs. Looking through their feminist lens applied to this form of knowledge production can yield a better understanding of the use of indicators in shaping evidence-based policy from the global level. In recognizing the value of quantification and data-driven evidence in policy, this article speaks to the tension between feminist critique of quantitative knowledge production and the feminist approach’s welcoming of multiple ways of knowing.
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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 | 0.029 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.053 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".