Bibliographic record
Abstract
Canadians often say to me, “You must be a Canadian or Permanent Resident, if you have been living here for more than a decade.” I tend to lose my words. Both my mother tongue and broken English don’t seem sufficient to explain. Where should I begin to give a sense of an immigration system written by the logic of a neo-liberal nation-state? I would rather be a body of 미친년, a degraded fallen woman, so please don’t take me seriously until I call you out and madly make out with you. I fell in love with her body free from legitimate bodies’ acceptance, a deceptive notion of consent. She utters fearlessly, not because her language is correct and direct, but because her language is doomed to be corrected and redirected. She dares to speak in front of you—the Indigenous peoples of this land, in the face of you—the settlers in this land, and in the midst of you—the migrants on this land. She picks up random words and spits them out. Dildos. Drones. Missiles. Canada’s Comprehensive Ranking System … She calls them out without grasping them. I gave her my desires, memories, and traumas that are seemingly lost because they barely reach the surface of her consciousness. Maybe she is a broken language floating on the surface of the water where your ‘Ophelia’ is perpetually drowning.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.015 | 0.009 |
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; both teacher heads agree on what is shown here.
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".