Moving gender across, between and beyond the binaries: In conversation with Shona Bettany, Olimpia Burchiellaro and Rohan Venkatraman
Bibliographic record
Abstract
Abstract This panel discussion explores why marketing and consumer behavior has struggled to move beyond the binary, the importance of disrupting the conventional binaries to recognize gender/sex/ual diversity, and the challenges in so doing. It raises to the fore concerns about institutional pressures, sanitization of work, academic positionalities, everyday encounters of discrimination against gender/sex/ual diversity, and the emancipatory but oppressive dynamics of categories. Yet the panelists also reflect on ways to challenge binaristic thinking. Just being in the academy and doing (small but) meaningful acts of institutional activism can produce ripple effects and open pathways for a better articulation of lived experiences and realities.
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 | 0.013 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.039 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.011 | 0.030 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".