The state of urban research: Views across the disciplines
Bibliographic record
Abstract
An “urban” subfield exists in virtually every social science discipline, but these subfields seldom engage one another. We asked scholars from five urban subfields to respond to questions about the state of urban research within their discipline. This article consists of their consequent essays and reflections on their responses. The questions posed included the discipline’s conception of “urban,” the main concerns motivating the subfield, the primary methodologies pursued, the extent to which their subfield interacted with or was informed by research in other urban subfields, and the main concepts or approaches it had to offer to other subfields or might take away from them. In our reflections, we particularly note the intellectual and institutional difficulties in creating a broader field of urban research or of engaging in truly inter-disciplinary research. We also highlight the desirability of greater engagement across these subfields through encouraging a “republic of conversation” among them.
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 | 0.057 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.020 | 0.091 |
| Scholarly communication | 0.032 | 0.020 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".