Bibliographic record
Abstract
List of Figures. List of Plates. List of Tables. List of Contributors. Preface. 1. Introduction: Reading Neoliberalizations: Kevin Ward and Kim England. Part I: Mainstream Economic Development and its Alternatives:. Introduction to Part I. 2. Competing Capitalisms and Neoliberalism: the Dynamics of, and Limits to, Economic Reform in the Asia-Pacific: Mark Beeson. 3. Neoliberalizing the Grassroots? Microfinance and the Politics of Development in Nepal: Katherine N. Rankin and Yogendra B. Shakya. Part II: Within and between State and Markets: the Role of Intermediaries:. Introduction to Part II. 4. Learning to Compete: Communities of Investment Promotion Practice in the Spread of Global Neoliberalism: Nicholas A. Phelps, Marcus Power, and Roseline Wanjiru. 5. Temporary Staffing, Geographies of Circulation, and the Business of Delivering Neoliberalization: Kevin Ward. 6. Neoliberalizing Argentina? Pete North. Part III: States and Subjectivities:. Introduction to Part III. 7. Neoliberalizing Home Care: Managed Competition and Restructuring Home Care in Ontario: Kim England, Joan Eakin, Denise Gastaldo, and Patricia McKeever. 8. Spatializing Neoliberalism: Articulations, Recapitulations, and (a Very Few) Alternatives: Catherine Kingfisher. 9. Co-constituting After Neo-liberalism: Political Projects and Globalizing Governmentalities in Aotearoa, New Zealand: Wendy Larner, Richard Le Heron, and Nicholas Lewis. 10. Conclusion: Reflections on Neoliberalizations: Kim England and Kevin Ward. Bibliography. Index
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.162 | 0.032 |
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".