From Hidden Geographies to “Lived, Possible, and Imaginative Geographies”: Making a Just City, for Whom and Where?
Bibliographic record
Abstract
Continuing from a well-received The Professional Geographer Focus section on hidden geographies published in 2023, this sequel invites authors to contribute further to the theme, focusing on hidden geographies in cities. This opening article begins by laying out the context, including recent theoretical turns, a call to shift attention to what makes a just city, contestations of the concept of (social) justice, and calls for inclusive theorization and attention to intersectionality. A search in The Professional Geographer since 1949 generates a lineage of powerful scholarship on social justice and the city, with ethical calls and interventions from marginalized geographers and those from outside the North American centers of the profession. This essay also introduces four article by geographers and urban scholars based on their field work across five continents (on urban ableism in U.S. and European cities, caring for indebted migrant workers in Dubai, infrastructural violence and oppression of low-income fathers in greater Johannesburg, and LGBTQ refugees and queer transnational communities in Buenos Aires). The articles enable a praxis of mainstreaming the “lived, possible, and imaginative geographies” and of what justice, for whom, and where.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.083 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.006 |
| 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".