Assembling Urbanism: Following Policies and ‘Studying Through’ the Sites and Situations of Policy Making
Bibliographic record
Abstract
Recent years have seen a challenge to the territorial orthodoxy in urban studies. An interest in policy assemblage, mobility, and mutation has begun to open up ‘the what’ and ‘the where’ of urban policy making. Unfortunately—but perhaps not surprisingly—theoretical developments and empirical insights have run ahead of significant methodological considerations. This paper turns to some of the methodological consequences of studying the chains, circuits, networks, and webs in and through which policy and its associated discourses and ideologies are made mobile and mutable. It focuses on three rubrics under which methodological decisions can be made: ‘studying through’ (rather than studying up or down), techniques of following actors, policies, etc, and relational situations in which mobilization and assemblage happen. The paper concludes with a brief reflection on how academic research design and writing assemble cities and urban policy making in ways that parallel the assembling practices of policy actors.
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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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.014 | 0.098 |
| Scholarly communication | 0.019 | 0.036 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".