Bibliographic record
Abstract
For me, rhythm means having consistency. The piece highlights my own experience with the disruption of my daily rhythm due to COVID-19. The first half shows my routine and interactions prior to COVID-19 while the second half shows my experiences in the present day. Prior to the virus, I had a day to day routine that was filled with noise. Everyday moved quickly and I established a daily rhythm. However, when COVID-19 spread, it changed everything. I felt like I didn’t have a routine anymore because I wasn’t allowed to go anywhere. Time was moving much slower and worst of all, xenophobia was growing at a significant rate. As a Chinese Canadian, this was the first time I truly felt the weight of the color of my skin. COVID-19 changed the way that I consistently assumed that the color of my skin wasn’t something that strangers would significantly care about. However, as I got on a bus, I unintentionally scared a woman simply because of my skin color. From that point, I knew that xenophobia would affect the way people perceived me everyday. The woman was scared of the virus— which in turn was scared of me—and I was scared that she would thwart her anger towards me because I am Chinese. If looks could kill, then the woman and I ironically both feared each other. Now, due to COVID-19, I am adapting to a new routine. A routine where the color of skin rings louder than any other sound.
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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.136 | 0.062 |
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".