Bibliographic record
Abstract
In the beginnings of the COVID-19 pandemic, Asians wearing masks in white colonized countries became a target of white supremacist violence. This piece of fabric has been a point of contention for anti-maskers who feel like their freedoms are being threatened. To dispel the myths of why people might wear masks, this is a basic 101 tutorial on how to use a mask. The comic tutorial breaks down what this apparently exotic cultural artifact is, why people wear it and how to use it properly. This is directed at white people so that next time they see East Asians wearing face masks, they will be equipped with all the relevant information to NOT punch us in the face, kick us out of supermarkets, bully Asian hospital workers, or abuse us on the streets or online. This comic was inspired by the safety instruction cards on flights and the patronizing posters in Anglo colonial university washrooms that tell Chinese students not to stand on the toilet seats. Thanks to Kirsty Fong for the idea and for being a reference.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.050 | 0.027 |
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".