Gender-Responsive Public Procurement in Africa: Barriers and Challenges
Bibliographic record
Abstract
Abstract Public procurement is often used to achieve policy goals beyond the purchase of the required goods and services. These goals include the economic advancement of minorities, the promotion of fair labour practices and climate action. In the last two decades, many countries have used public procurement to advance gender equality. This is referred to as gender-responsive procurement and is often implemented through the award of public contracts to women-owned businesses. While many countries have legal provisions designed to increase the award of public contracts to women, gender-responsive procurement is extremely limited and women-owned businesses are not fully integrated into public sector supply chains. This is unfortunate, given that gender-responsive procurement can improve women's economic empowerment, with implications for sustainable development. This article adopts a gender equality and women's economic empowerment lens to examine the legal, policy and cultural barriers to gender-responsive procurement and recommends measures to improve the award of public contracts to women-owned businesses.
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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 | 0.026 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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".