Bibliographic record
Abstract
Fears, for many, have been actualized during the global pandemic. Working through borders and geographical crossings, this article performs a constellation of my anxieties about race, culture, and academia. I explore what kinds of feminisms might leak through if we let ourselves fall into fear. Through autotheory, a way of theorizing from my life experience, I revisit domains of seemingly bygone issues of feminism through encounters of distress and unease, marked through the performance of public/private breastfeeding and pumping. I put tension on feminist configurations of materiality as they rub against my memories. Alongside this, I analyze the tactics offered by the video and performance works of Patty Chang. The article mobilizes Patty Chang work Milk Debt, which presents through performers pumping breastmilk while reciting solicited fears from people living across myriad geographies during COVID-19. I suggest my fears alongside Chang’s performances of fear agitate the ways in which colonialism, elitism, and racial power persist in feminist circles—these fears assemble what I call a porous feminism that enables unruly minoritized feminisms to leak through.
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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.067 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".