Global Ideologies and Urban Landscapes
Bibliographic record
Abstract
1. Global Ideologies and Urban Landscapes: Introduction Manfred B. Steger (RMIT University, Professor of Global Studies) and Anne McNevin (RMIT University, Research Fellow) 2. After Neoliberalization? Neil Brenner (New York University, Professor of Sociology and Metropolitan Studies), Jamie Peck (University of British Columbia, Canada Research Chair in Urban and Regional Political Economy) and Nik Theodore (University of Illinois, Chicago, Associate Professor and Director of the Center for Urban Economic Development) 3. Provoking 'globalist Sydney': neoliberal summits and spatial reappropriation James Goodman (University of Technology Sydney, Associate Professor) 4. Toronto's Distillery District: Consumption and Nostalgia in a Post-Industrial Landscape Margaret Kohn (University of Toronto, Associate Professor) 5. Delhi: Global mobilities, identity and the postmodern consumption of place Chris Hudson (RMIT University, Senior Lecturer) 6. Materializing the Metaphors of Global Cities: Singapore and Silicon Valley Terrell Carver (University of Bristol, Professor of Political Theory) 7. Gaming Space: Casinopolitan Globalism from Las Vegas to Macau Timothy W. Luke (Virginia Polytechnic Institute and State University, Distinguished Professor of Political Science) 8. Border Policing and Sovereign Terrain: The Spatial Framing of Unwanted Migration in Australia and Melbourne Anne McNevin (RMIT University, Research Fellow) 9. Hong Kong and Berlin: Alternative Scopic Regimes Michael J Shapiro (University of Hawaii, Professor of Political Science) 10. An Emergent Landscape of Inequality in Southeast Asia: Cementing Socio-Spatial Inequalities in Viet Nam James H. Spencer (University of Hawaii at Manoa, Associate Professor)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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; both teacher heads agree on what is shown here.
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".