Democratizing referrals: Market transition and labor market networks in China
Bibliographic record
Abstract
The effects of marketization on the use of labor market networks remain poorly understood and the underlying mechanisms - unclear. We depart from previous scholarship by examining these issues from the perspective of the referrer - that is, the person providing information about job opportunities to potential candidates - rather than that of the job seeker or the employer. Adopting the perspective of referrers allows us to disentangle the effects of two distinct mechanisms - changing firm practice and changing social expectation - that the literature suggests shape labor market networks in transition economies. Using a large-scale nationally representative survey of Chinese urban residents, we find job-referring is responsive to the introduction of firm practices encouraging referrals. Nevertheless, the likelihood of referring declines with marketization. We show that this decline is due to the association between marketization and the decrease in the likelihood that those in positions of discretionary authority refer, a change that points to changing social expectations surrounding social networks. Our results have implications for our understanding of a) the effects of marketization on labor market networks and b) conflicting roles of labor market intermediaries.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".