Job seekers' attitudes toward cybervetting in China: Platform comparisons and relationships with social media posting habits and individual differences
Bibliographic record
Abstract
Abstract Cybervetting, or reviewing applicants' social media profiles, has become a central part of the hiring process for many organizations. Yet, extant cybervetting research is largely limited to Western platforms and samples. The present study examines the three core elements of attitudes toward cybervetting (ATC—perceived justice, privacy invasion, and face validity) using a sample of 200 Chinese job seekers providing their views on three popular platforms in China (WeChat, QQ, and Weibo). Attitudes were negative across all platforms, although slightly more positive for WeChat. ATC were associated with job seekers' social media posting habits (e.g., posting positive content more frequently) and individual differences (i.e., gender and extraversion). Organizations should be mindful that cybervetting might impede the recruitment of talents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".