Cybervetting and the Public Life of Social Media Data
Bibliographic record
Abstract
<p>The article examines whether and how the ever-evolving practice of using social media to screen job applicants may undermine people's trust in the organizations that are engaging in this practice. Using a survey of 429 participants, we assess whether their comfort level with cybervetting can be explained by the factors outlined by Petronio's communication privacy management theory: culture, gender, motivation, and risk-benefit ratio. We find that respondents from India are significantly more comfortable with social media screening than those living in the United States. We did not find any gender-based differences in individuals' comfort with social media screening, which suggests that there may be some consistent set of norms, expectations, or "privacy rules" that apply in the context of employment seeking-irrespective of gender. As a theoretical contribution, we apply the communication privacy management theory to analyze information that is publicly available, which offers a unique extension of the theory that focuses on private information. Importantly, the research suggests that privacy boundaries are not only important when it comes to private information, but also with information that is publicly available on social media. The research identifies that just because social media data are public, does not mean people do not have context-specific and data-specific expectations of privacy.</p>
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.009 |
| 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".