Mainstream Psychology And The Gender Binary: Toward An Account Of Becoming "Non-Binary"
Bibliographic record
Abstract
The gender binary haunts mainstream psychology’s history of medicalizing trans and gender nonconforming people, particularly its construction of their gender identities as psychopathological and in need of treatment for violating the binary logic of normative (cis) development. Drawing on interviews with 24 participants who identified as “non-binary,” this dissertation advances: 1) a genealogical analysis of the construction, interpretation, and administration of “transgenderism” (psychology’s parlance) which elucidates the discipline’s maintenance of the gender binary through said construction, interpretation, and administration; and (2) an account of “becoming” gendered (non-binary, in this case) as an alternative to the mainstream models of gender identity development. Becoming (a) shifts from the etiological “why” to the psychosocial “how” (as in, how to go about assembling oneself as non-binary; labels and pronouns are key); (b) eschews teleology (there is no end goal with regard to embodiment); (c) privileges gender self-determination; (d) attends to intersectionality; and (e) foregrounds intersubjectivity. The participants were largely concerned with asserting the validity of their gender identities as non-binary, which are routinely dismissed and invalidated, and this dissertation works toward undoing psychology’s own invalidating practices.
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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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.072 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".