You're hired: examining acceptance of social media screening of job applicants
Bibliographic record
Abstract
The paper examines attitudes towards employers using social media to screen job applicants. In an online survey of 454 participants, we compare the comfort level with this practice in relation to different types of information that can be gathered from publicly accessible social media. The results revealed a nuanced nature of people’s information privacy expectations in the context of hiring practices. People’s perceptions of employers using social media to screen job applicants depends on (1) whether or not they are currently seeking employment (or plan to), (2) the type of information that is being accessed by a prospective employer (if there are on the job market), and (3) their cultural background, but not gender. The findings emphasize the need for employers and recruiters who are relying on social media to screen job applicants to be aware of the types of information that may be perceived to be more sensitive by applicants, such as social network-related information. Keywords : social media, information privacy, job screening, hiring practices
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".