Bibliographic record
Abstract
rom the late 1950s to the mid-1980s, jobs in China were assigned by the state and employment in urban areas was secure and intended for life. Although underemployment was a problem and there were serious job shortages during and after the Cultural Revolution that started in 1966, once an individual was assigned a position, he or she could expect it to be stable and for life. During this period, when wages and salaries were dictated by the central government and kept universally low,jobs often carried cradleto-grave welfare benefits, including pension, housing and free medical care for both workers and their dependents. While wage differences were small among different occupations, benefits differed greatly among different types of work organizations.' Government agencies, public organizations and state firms provided their employees with all of the above-mentioned benefits; collective firms owned by local governments often offered pensions, but no housing and only partial medical coverage. Employment in government agencies and state firms was also more prestigious than that in collectivelyowned firms, even though workers in both firms enjoyedjob security. Because the government considered industrial production more important than consumer or personal services, service enterprises such as shops and restaurants were mostly collective, with low pay, few welfare benefits and low prestige.2 This socialist employment policy, coupled with a government prohibition against rural-to-urban migration, led to social stability and a near absence of such modern urban ills as high crime rates, slums, homelessness, drug abuse and prostitution. The negative consequences of such a system, on the other hand, were low productivity, a stagnant economy, a severe
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 | 0.001 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".