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Record W4402442902 · doi:10.5194/gh-79-283-2024

Urban geography in crisis times: insights from a feminist project

2024· article· en· W4402442902 on OpenAlexafffund
Linda Peake, Mantha Katsikana, Grace Adeniyi-Ogunyankin, Anindita Datta, Swagata Basu, Karen de Souza, Penn Tsz Ting Ip, Joy Marcus, Carmen Ponce, Nasya S. Razavi, Araby Smyth, Biftu Yousuf

Bibliographic record

VenueGeographica Helvetica · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicGender Politics and Representation
Canadian institutionsUniversity of OttawaMount Allison UniversityQueen's UniversityIntertek (Canada)York University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsUrban geographyGeographyEconomic geographyHuman geographyUrban planningCivil engineeringEngineering

Abstract

fetched live from OpenAlex

Abstract. In this short intervention addressing the impact of crises on geographical knowledge practices, we, members of GenUrb (a multi-sited, longitudinal, partnered urban research project), ask, “what counts as crisis?”, sketching out epistemological and methodological points about our project's engagement with this call. We query the adeptness of dominant Eurocentric epistemologies in addressing crises, adopting the work of Bedour Alagraa, who places crises firmly within a historical–geographical colonial framing that conceptualizes crises not through rupture but through continuation. We illustrate the utility of this epistemological framing of crisis, honing in on the everyday violence that women continually experience, with our research in the cities of Cochabamba, Delhi, Georgetown, Ibadan, Ramallah, and Shanghai, showing that one in every two women participants had experienced intimate partner violence. We further ask what crises mean for the methodologies we adopt, specifically concerning questions of the co-production of knowledge and methods.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.693
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.311
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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