Bibliographic record
Abstract
Preface:. From the 'New Localisma to the Spaces of Neoliberalism: Neil Brenner (New York University) & Nik Theodore (University of Illinois at Chicago). Part I: The Urbanization of Neoliberalism: Theoretical Foundations:. 1. Cities and the geographies of 'actually existing neoliberalisma : Neil Brenner (New York University) & Nik Theodore (University of Illinois at Chicago). 2. Neoliberalizing space: the free economy and the penal state: Jamie Peck (University of Wisconsin--Madison) & Adam Tickell (University of Bristol). 3. Neoliberalism and socialisation in the contemporary city: opposites, complements and instabilities: Jamie Gough (University of Northumbria). 4. New Globalism, New Urbanism: Gentrification as Global Urban Strategy: Neil Smith (CUNY Graduate Center). Part II: Cities and State Restructuring: Pathways and Contradictions:. 5. Liberalism, Neoliberalism and Urban Governance: A State--Theoretical Pespective: Bob Jessop (Lancaster University). 6. Interpreting Neoliberal Urban Policy: The State, Crisis Management, and the Politics of Scale: Martin Jones (University of Wales) & Kevin Ward (University of Manchester). 7. 'The city is dead, long live the networka : Harnessing networks for the neoliberal urban agenda: Helga Leitner (University of Minnesota) & Eric Sheppard (University of Minnestota). 8. Extracting Value from the City: Neoliberalism and Urban Redevelopment: Rachel Weber (University of Illinois at Chicago). Part III: New Geographies of Power: Exclusion and Injustice:. 9. Neoliberal urbanization in Europe: large scale urban development projects and the new urban policy: Erik Swyngedouw (Oxford University), Frank Moulaert (University of Lille) & Arantxa Rodriguez (University of the Basque Country). 10. Retro--Urbanism: Reliving the Dreams of 1980s Neoliberalism in Toronto, Canada: Roger Keil (York University, Toronto). 11. Spatializing injustice in the late entrepreneurial city: Unraveling the contours of Britaina s revanchist urbanism: Gordon MacLeod (University of Durham).
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.030 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".