Bibliographic record
Abstract
Purpose – The purpose of this paper is to analyse social media issues that give rise to employment-related legal and ethical dilemmas, with reference made to recent case law development, and offer recommendations for employers and employees. Design/methodology/approach – Prior research, statistical trends, and case laws are reviewed. Findings – Employers using social media for employment decisions may risk crossing the lines of discrimination, infringement on personal privacy, and/or interference with employees’ concerted activities protected by US law. However, employers not using social media may face negligent hiring and damages for improper employee messages posted. For employees, while social media provides a connection tool, messages posted off-duty and thought to be “private” may still be used as evidence in support of disciplinary actions. Practical implications – Employers, employees, and their unions must be cognizant of the ethical and legal implications of using social media in the employment context, and the latest developments in the privacy rights, human rights, labour relations rights, and contractual rights. Concerns about power shift need to be addressed. Social implications – Social media growth has blurred the boundary between work and private lives. With employers able to monitor employees’ social media activities almost at all times, this has implications for the overall power and control. On the other hand, employees may find social media offering another voice channel that can also potentially increase their power to some extent. Originality/value – Social media is a fast developing area with new case laws emerging regarding its use in the employment context. The paper provides a systemic review of the issues and latest developments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.027 | 0.050 |
| Scholarly communication | 0.025 | 0.021 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.017 | 0.009 |
| Insufficient payload (model declined to judge) | 0.011 | 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".