Queer Economic Geographies: Sexual Hegemony, Queer and Trans Work, and Homocapitalism
Bibliographic record
Abstract
Abstract While queer and trans perspectives and theories are significant themes in most areas of critical human geography today, the same cannot be said of economic geography. This paper argues for the importance of theorising the relations between non‐normative sexualities and gender identities in economic geography. This argument builds on research in feminist economic geography and beyond about the centrality of cisheterosexuality to the economy and structures of capitalism. I show how queer and trans geography and feminist economic geography have already contributed to research about the relations between non‐normative sexualities and gender identities and the economy, then outline three queer economic geographies as examples of how economic geography might further engage with sexuality beyond cisheterosexuality: (1) sexual hegemony; (2) queer and trans work; and (3) homocapitalism. Together with existing research on the economy in queer and trans geography, these queer economic geographies—each touching on concepts central to economic geography—contribute further to our understandings of how non‐normative sexualities and gender identities can be theorised in economic geography.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.062 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".