The Influence of Employers' Use of Social Networking Websites in Selection, Online Self‐promotion, and Personality on the Likelihood of <i>Faux Pas</i> Postings
Bibliographic record
Abstract
Employers' selection practices sometimes involve reviewing applicants' profile on social networking websites (SNWs) and invading applicants' privacy (e.g., asking for their passwords). Applicants can be eliminated because of faux pas (i.e., inappropriate content) they post online. Yet, little research has examined factors related to faux pas postings. The present study examines employers' use of SNWs in selection, participants' internet and SNWs use, personality, and SNWs self‐promotion as predictors of the likelihood of faux pas postings. Results show lower likelihood of faux pas postings when participants are informed that a high proportion of employers use SNWs in selection, but mainly when it includes invasion of applicants' privacy. Moreover, participants' age, privacy settings, extraversion, and SNWs self‐promotion are related to faux pas.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".