Bibliographic record
Abstract
Public-Private Partnerships (PPPs) have gained a renewed momentum in recent years, and have come to be viewed by governments and funders alike as a silver bullet for infrastructure development and public service provision. Critiques of the corporate capture of development are well established, yet until now the urgent question of the impacts of PPPs on women's human rights around the world has remained under-explored. This open access book aims to fill the gap, providing new insights from a set of case studies from across the Global South. Bringing an intersectional feminist approach to PPPs, these cases enable analysis that can inform advocacy and activism, whilst challenging dominant narratives and resisting the negative impacts of PPPs on women and historically marginalized communities' human rights. Widely advocating for stronger regulatory frameworks and institutions, and indicating how changes could be implemented, the examples analysed cover a range of sectors including health, energy, and infrastructure from countries including Ethiopia, Peru, India and Fiji. The eBook editions of this book are available open access under a CC BY-NC-ND 4.0 licence on bloomsburycollections.com. Open access was funded by Development Alternatives with Women for a New Era (DAWN).
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".