Bibliographic record
Abstract
For urban historians and urban historical geographers, the relevance and meaning of the city as a driver of human history is central to what we do, both theoretically and empirically. For some, the question of how to define what a city does is a pressing one. For many of us, though, the question is rarely raised; it resides in that murky place behind our writing and thinking, and has little direct or conscious play over how we go about doing our daily work. Historical geographers, with their greater emphasis on theory and spatial relations, are more likely than historians, trained as they are to think through narrative, empirical evidence and temporality, to explore the city's role in explaining social change. Despite this difference, the fact remains that only a handful of urban historical scholars of whatever stripe are actively interested in thinking through the scope and significance of urban agency. The fact that few openly grapple with the question of urban agency, of course, does not mean that we do not work with some understanding of the city's ontological status. All of us do, for better or worse.
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 | 0.010 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.028 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.030 | 0.030 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".