3The Labor Politics of Market Socialism Collective Inaction and Class Experiences Among State Workers in Guangzhou
Bibliographic record
Abstract
Labor has become an economic and political challenge in a China plagued by unemployment among the ranks of state workers, the proclaimed masters of the Chinese state. Five and a quarter million urban state workers are officially registered as unemployed, and another estimated twenty million have become surplus or &dquo;off-duty&dquo; (xiagang) workers (Zhongguo laodongbao, 9 Nov. 1996). Labor offi-cials have responded by launching a nationwide reemployment cam-paign since mid-1995 to absorb these laborers, mainly by developing labor-intensive, tertiary sectors such as food services, transport, do-mestic services, retailing, and tourism (Guangdong laodongbao, 3 Dec. 1995). Official recognition of the massive number of the unem-ployed and the redundants, who are referred to as the &dquo;two categories of staff&dquo; in the Chinese press, reveals only the tip of the iceberg. The plight of the state workers is also to be found in the increasing number of &dquo;impoverished workers,&dquo; the new urban poor whose wages or pensions are falling far behind the rising cost of living. A survey of thirteen provinces has found that more than 10 % of workers are &dquo;in dire straits&dquo; in five provinces and 5 % to 10 % in another six. Unpaid salaries and pension entitlements have affected the lives of millions
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".