MétaCan
Menu
Back to cohort
Record W4411792675 · doi:10.1080/0966369x.2025.2516848

Queering feminist geography II: working through and working against trans-exclusionary feminisms

2025· article· en· W4411792675 on OpenAlexaff
Queering Feminist Geography Collective, Eden Kinkaid, Wiley Sharp, Sarah Fogel, Aila Bandagi Kandlakunta, Gabi Kirk, Lindsay Naylor, LaToya E. Eaves, N.H. Koenig, Ingrid L. Nelson, Niiyokamigaabaw Deondre Smiles, Kelsey Emard

Bibliographic record

VenueGender Place & Culture · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsUniversity of VictoriaUniversity of Toronto
Fundersnot available
KeywordsGender studiesSociologyFeminism

Abstract

fetched live from OpenAlex

In the first Viewpoint in our Queering Feminist Geography series (QFG), we introduced our queer, trans, and feminist geography collective. We described how we have been brought together by our shared commitment to support queer/trans life within feminist geography, geography as a whole, and the wider world. In this second Viewpoint, we develop another key context for understanding why, as feminist geographers, we have come together: the rise of trans-exclusionary feminism, which we understand as one facet of white feminism. We have been alarmed – as feminist scholars and activists – by the global rise of feminist movements premised on trans exclusion and cisnormativity. As a coalition of cis, queer, and trans feminists, we feel it is our responsibility to critically reflect upon these developments and equip ourselves to work against them. In this Viewpoint, we open up a space for a much needed dialogue in feminist geography: the question of where trans people fit in our tradition and our political visions. In the following Viewpoints, we discuss strategies for queer/trans allyship (QFG III) and the scholarly and activist potentials of trans-feminist coalition within and beyond geography (QFG IV).

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.131
GPT teacher head0.439
Teacher spread0.308 · 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.

Study designQualitative
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

Citations2
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueGender Place & CultureSame topicQualitative Research Methods and EthicsFrench-language works237,207