A Book Of One's Own: Gender Diversity and Non Binary Identities Represented in Recent Literature
Bibliographic record
Abstract
Non binary representations openly discussed in literature are a rather recent phenomenon, which goes hand in hand with the social changes witnessed in these past decades.In this essay, books such as Gender Failure (Coyote, Spoon, 2014), Symptoms of Being Human (Garvin, 2016), None of the Above (Gregorio, 2015), I Wish You All the Best (Deaver, 2019) will be analyzed in order to explore how these examples introduce the reader to different perspectives which escape gender normativity. For this purpose, we will discuss a variety of topics, including pronouns and language representations, visibility of non binary experiences, discrimination and resisting binary norms. Authors related to gender discourse, for example, Judith Butler, Riki Wilchins and Mauro Cabral, will be consulted.BibliographyButler, J. (1988) Performative Acts and Gender Constitution: An Essay in Phenomenology and Feminist Theory. Theatre Journal 40, no. 4.Cabral, M., Benzur, G. (2005) Cuando digo intersex: un diálogo introductorio a la intersexualidad. I. W. Gregorio (2015) None of the Above. California: Balzer + Bray. Spoon, Rae, and Ivan Coyote. (2014) Gender Failure. Vancouver: Arsenal Pulp Press.Wilchins, R. A. (2004) Queer theory, gender theory: an instant primer. Los Angeles: Alyson Books.Garvin, J. (2016) Symptoms of Being Human. California: Balzer + Bray. Mason D. (2019) I Wish You All the Best. North Carolina: Push.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| 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".