Getting Under Your Skin--Literally: RFID in the Employment Context
Bibliographic record
Abstract
This article explores the legal ramifications of the use of radio frequency identification chips (“RFID”) by employers. RFID is an automated data-capture technology that can be used to identify, track and store information contained on a tiny computer chip, which uses electromagnetic energy in the form of radio waves to communicate information. These chips can be implanted under an employee’s skin, worn in an employee’s clothing or in an identification badge. Part II presents a brief history of RFID, as well as novel and interesting uses in the workplace. This section also discusses security and safety concerns regarding the use of this technology. Part III analyzes current and proposed law in the United States regulating RFID, and privacy implications. Part IV details legal regulations in the international context, including in Canada, the European Union and Australia. Lastly, in Part V, recommendations about the use and legal regulation of RFID in the workplace are proposed.
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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.013 | 0.028 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| 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".