Constructing gender, constructing the urban: A review of Anglo-American feminist urban geography
Bibliographic record
Abstract
This essay explores the changing shape of Anglo-American feminist urban geography, through a discussion of material published in Gender, Place and Culture and elsewhere over the past decade. We contextualize this discussion in relation to the development of feminist urban studies since the 1970s, showing its enduring commitment to work across traditional analytical divides that obfuscate crucial aspects of the mutual constitution of gender and the urban. Focusing on two thematic areas--affective experiences of urban space, and the making of urban public spaces--we examine how this commitment is expressed in recent contributions to feminist urban geography. Both bodies of work successfully challenge a divide between scholarship that focuses on how cities constrain, disadvantage and oppress women, and scholarship that focuses on how cities liberate women. However, we are disturbed by a seeming bifurcation between work concerned with issues of recognition and work focusing on issues of redistribution, with the former being well represented in Gender, Place and Culture and the latter more likely to be aired in 'mainstream' journals. We conclude by reflecting on our lack of perspective on the trajectories of feminist urban geography outside of the Anglo-American context and ask whether the boundaries within which our review has been conducted are themselves gendered.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".