Bibliographic record
Abstract
I went to graduate school for many reasons, not all of which I was conscious of at the time.Toward the end of my undergraduate degree, I had been a bit listless.Coursework in history and French had held my attention, but living in Canada's capital city had disillusioned me to the empty liberal promises of the grown-up office jobs my peers were landing.I contemplated applying to law school but resented the idea of taking the law so seriously.Around that time my mentor, a historian of sexuality, called me into her office and slyly asked where I was going to apply for PhDs, as if it were a matter of fact that I would.And before I could shrug, she supplied an answer: the United States.There, I could study the history of sexuality and queer theory, two subjects I had fastened myself to in her classes.I remember walking home that day, a little wide-eyed at twenty years old, thinking she had changed the course of my life.I was fixated on the peculiar prospect of moving to the United States, but it was also the idea of getting a PhD itself.I grew up in a working-class family of Punjabis who had immigrated before Canada engineered its immigration policy to drain the world of its most educated.We weren't exactly the model minority trope, replete with doctorates and doctors.It was hard to see myself as a professor-to-be.Looking back, there was a submerged notion underneath each of those thoughts: I might go to graduate school to meet trans people.Part of the indescribable reward of working with trans graduate students is the gift of a set of experiences I didn't have while earning my PhD.When I teach specialized seminars on trans femininity, or the racial history of trans medicine, I think back on how I learned the field in two wildly asymmetrical installments.I was incredibly lucky to attend a seminar at Rutgers University in 2012-13, "Trans Studies: Beyond Homo/Hetero Normativities."Aren A. Aizura, then a postdoctoral fellow, was building the first trans studies infrastructure at Rutgers.He generously
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 | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.403 | 0.260 |
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".