Bibliographic record
Abstract
Today, cisgender (or cis for short) typically refers to someone whose gender “aligns with” or “matches” their sex assigned at birth. In this essay, I track and analyze how that dominant sense of cis arose. I identify three primary forces that helped mainstream the term and its current definition: Julia Serano’s Whipping Girl, Sam Killerman’s “Cisgender Privilege” checklist, and South Park’s “Cissy” episode. Analyzing these key moments, I argue that cis gets mainstreamed through its depoliticization and rarefaction. That is, it comes to modify a sense of self between the ears rather than serve as a militant term deployed in coalitional politics. I further argue that cis gets mainstreamed through a practice of citational injustice, according to which trans community conversations go largely unengaged and uncited. In closing, I argue that if we are to continue to use cis in trans, queer, feminist, and allied circles, it must be rerooted in the political history of trans community conversations and at the nexus of multiple co-constitutive assemblages of power-knowledge. Cis is best used as a term to diagnose a complex system of institutional legibility and legitimacy, not a private assessment of sex/gender.
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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.021 | 0.047 |
| Scholarly communication | 0.024 | 0.029 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.015 | 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".