Bibliographic record
Abstract
This manuscripT was drafTed during my second pregnancy, when references to labour in my personal and scholarly circles caused momentary confusion.Labour or labour?At the time of writing, labour, for me, was the work of birthing, research and writing, caring for family and friends, academic service, community organizing and activism, and teaching and learning from bright undergraduate students in the stolen and unceded territories of the Xʷməθkwəy'əm (Musqueam), Skwxwú7mesh (Squamish), and səĺilwətaʔɬ (Tsleil-Waututh) nations.For this work, I relied on a network of care, and here I wish to dispel the notion that sole-authored contributions are the work of one person.Thank you to my editors, James MacNevin and Ann Macklem, and the team at UBC Press for making this publishing process so rewarding while I raced towards a couple of due dates simultaneously.I owe my most sincere thanks to the peer reviewers of this manuscript for close, careful reading and feedback.Your thoughtful engagement has pushed me to consider the theoretical potential of coming undone as a feminist resistance project -of ordinary encounters and system change.
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 | 0.007 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.292 | 0.170 |
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".