Queering feminist geography II: working through and working against trans-exclusionary feminisms
Bibliographic record
Abstract
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 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 | 0.040 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.024 | 0.092 |
| Scholarly communication | 0.019 | 0.025 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".