Bibliographic record
Abstract
Dialogues in Urban Research was established to create critical yet constructive conversations about cities and urbanization at a perilous but fascinating historical-geographical conjuncture. In this vein, we thank our four interlocuters, Emma Colven, Renee Tapp, Delik Hudalah, Dallas Rogers, and Christopher Silver, for their provocative comments on our manuscript. There is much food for thought in their ideas. In response to their comments, we initially expound on three core themes in the article that address their concerns about our conceptual apparatus. Here we offer clarity to dispel any misunderstandings of what our paper is about. The discussion's cornerstone: Dracula urbanism as an important situated theorising; Dracula's complicated features, and the reality of smart city building as the leading edge of Dracula urbanism. Then, we illuminate the contributions of our critics as a collection of nuanced modifications and extensions of our work. We are heartened that these fellow urbanists, in this special journal issue, have critically appraised the Dracula urbanist concept and move it forward in meaningful ways.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 0.000 |
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 teacher head, 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".