Managing the Impact of New Media on the Employment Relationship
Bibliographic record
Abstract
Attention to privacy issues in the workplace has increased over the past two decades as use of electronic mail and text messages has made these means of communication commonplace. Beyond text messages and emails, employees can access the internet at their place of employment at many different entry points. This access can be through company issued desktops or laptops, mobile phones, mobile internet devices (MIDs), Smartphone technology (photography; video and voice recording capabilities; file transfer and storage), off-site internet connections, Wi-Fi access or hot spots. Employees can access and/or post information on various sites including blogs, wikis, RSS feeds, instant messaging (IM's), e-newsletters, Twitter (micro-blogging), YouTube, Facebook, cloud computing, podcasting, tagging, and Web 2.0 tools. These are all forms of “new media” or the new communication tools that are sweeping the employment world. What information is derived via New Media, what is discoverable and what is the impact on the employment relationship? How does developing case law affect this relationship? Employers and businesses that do not understand the importance and ramifications of these new communication tools may find that they have inadvertently opened the door to litigation and liability, or loss of profit and/or loss of competitive advantage. Companies also increase their risk of exposure to spam, phishing or malware attacks; risk loss of proprietary information, sensitive data and proprietary information. This paper examines how the employment relationship is impacted by “new media” given current social research and developing federal and state case law, including City of Ontario, California v. Quon, O'Connor v. Ortega, and Stengart v. Loving Care Agency.
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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.028 | 0.023 |
| Open science | 0.003 | 0.021 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.024 | 0.005 |
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".