Dialogues in urban and regional planning
Bibliographic record
Abstract
1. Introduction: Seizing the Opportunity Thomas Harper, Heloisa Costa and Anthony Yeh 2. Portraying, classifying and understanding the emerging landscapes in the post-industrial city Aspa Gospodini 3. Water and Urban Sustainability in the Metropolitan Area of the Valley of Mexico Virginia Lahera Ramon 4. China's urban developmental planning in rapid urbanization: Resource mobilization and responsiveness to market change Jieming Zhu 5. New Urbanism and Sprawl: A Toronto Case Study Andrejs Skaburskis 6. Reimagining inner-city regeneration in Hillbrow, Johannesburg: Identifying a role for faith-based community development Tanja Winkler 7. Town planning versus urbanismo Michael Hebbert 8. Paris burns: Architecture or revolution? Ester Limonad 9. On the edge of reason: Planning and urban futures in Africa Philip Harrison 10. Territorial planning and the national project: The challenges of fragmentation Carlos Vainer 11. Planning styles in conflict: The metropolitan transportation commission Judith E. Innes and Judith Gruber 12. Performance-Based Planning: Perspectives from the United States, Australia, and New Zealand Douglas C. Baker, Neil G. Sipe, and Brendan J. Gleeson 13. The logic of critical communicative planning: Transaction cost alteration Tore Sager 14. Planning appeals: Are third party rights legitimate? the case study of Victoria, Australia Stephen Willey
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 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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.027 | 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".