Bibliographic record
Abstract
To wrap up a tumultuous year in Chinese \nlabour and civil society, we are pleased to \nannounce the publication of the fourth issue \nof Made in China. Among the most notable \nevents in the last quarter are the protests \nthat erupted at several plants in China \nowned by multinational companies, such \nas Coca-Cola, Danone, and Sony, following \nthe announcement that the factories would \nbe sold to local Chinese companies. Other \nsignificant happenings include a series of \nblasts at Chinese coal mines that claimed \ndozens of lives and prompted widespread \npublic questioning of the commitment of \nthe authorities to workplace safety in the \nmining sector, as well as the sentencing \nof Meng Han�the last labour activist on \ntrial as part of the crackdown of December \n2015�to twenty-one months in prison. \nIn the China Columns section, we present \nthree essays that offer distinct perspectives \non how the party-state manages and \ncontrols an increasingly unequal and \nfractured society.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.282 | 0.105 |
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".