Bibliographic record
Abstract
It is an understatement to say that gender is complicated, but when the idea of transgenderism is introduced, notions of gender become utterly bewildering to many people.In her book Transgender History, Susan Stryker succinctly explains why transgender people are often treated with open disdain and disrespect in society: "Because most people have great difficulty recogniz ing the humanity of another person if they cannot recognize that person's gender, the gender-changing person can evoke in others a primordial fear of monstrosity, or loss of humanness" (p. 6).Indeed, there are strong male and female signifiers that humans are conditioned to recognize from a very early age, and we become confused when those signifiers are distorted.Facial hair?Male.High heels and breasts?Female.But what of the person with facial hair in heels?Assumptions about gender start at birth: "Congratulations!Boy or girl?"There is a strong social desire to immediately classify something new, and it begins with needing to know whether blue or pink blankets should be used, and if family and friends should start purchasing toy dolls or toy trucks.Gender assumptions and generalizations are difficult to get away from, and this is precisely what Stryker addresses: What is gender?What is sexual identity?The quick and dirty answer: it is complicated.As Stryker explains: "Because transgender issues touch on fundamental questions of human exist ence, they take us into areas that we rarely consider carefully" (p. 7).Transgender History is a compact, thorough introduction to modern transgender history, concentrating mainly on the United States (though European transgender history is also explored; the reader is duly informed that the first recorded "sex change" operations took place in Europe).The actual text is a relatively short 153 pages, divided into five chapters, and accompanied by a reader's guide, a wealth of suggested readings, detailed sources, and an index.The author's qualifications are impressive: Stryker has held positions at both
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".