Bibliographic record
Abstract
I saw them in an evening in 2021 when I passed by a clothes shop in Chaoyang’s famous hutong, or narrow alleys formed by traditional courtyard houses, during one of the many lockdown heights in Beijing that rarely paralyzed the entire city. To keep the economy running, individuals who momentarily shared a public space such as subways, shops, or schools with someone who tested positive, as indicated by the health codes we scanned everywhere we went, were put into 14-day quarantines without shutting down the neighborhood. The conversation I heard between the women went as follows: Customer: “So, you’re on lockdown?” Saleswomen: “Yes.” Customer: “You’d be here for 14 days?” Saleswomen: “Yes.” Customer: “How do you shower or sleep?” Saleswomen: “There’s a sink in the back to wash our face.” To protect public health, the public was ironically locked into unhealthy environments. Considering the inflation, artificial shortages in necessities, recession-induced layoffs, forced overtime in closed factories where most workers were quarantined at, and continued work that increased the risk of exposure, all without any financial assistance, the working class always bears the blunt of the impact of government and capitalist growth policies. This work is the third in my series called “Progress,” which contains snapshots of my personal life, a glimpse, or a momentary revelation that gives context and complexity to the well-acclaimed liberalization of Chinese economy.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.732 | 0.685 |
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".