Blurred Boundaries: Social Media Privacy and the Twenty-First-Century Employee
Bibliographic record
Abstract
This paper discusses the future of employee privacy in social media. Part I reviews the extant legal landscape with an emphasis on three general areas of employer activity related to employees’ online activities: (1) monitoring and surveillance of employee social media profiles, (2) evaluation of applicants’ social media profiles and online speech in making hiring decisions, and (3) limiting employees’ off-duty online activities. Part II reports the results of an empirical research project into the expectations of young employees regarding the role of social media in the workplace. We asked respondents about a wide range of topics related to social media, such as the extent of personal information they post online, the privacy-protective measures they employ on social media sites, their level of concern regarding their privacy online, and their attitudes and expectations regarding the use of social media in the workplace. Despite granting employers access to information about their private lives by participating online, respondents expect that work life and private life should be generally segregated — and that actions in one domain should not affect the other. Guided by the survey findings and legal examples from international jurisdictions, in Part III we offer workable recommendations designed to protect employees’ desire to maintain some separation between personal and professional contexts.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".