Secret Code: The Need for Enhanced Privacy Protections in the United States and Canada to Prevent Employment Discrimination Based on Genetic and Health Information
Bibliographic record
Abstract
The collection of genetic and health information by employers for reasons that are unrelated to the health and safety of workers is an undue infringement of the right to privacy, and consequently should be firmly prohibited by statute. Comprehensive genetic and health information privacy requires the protection of at least three critical elements of the right to privacy--namely choice, secrecy, and confidentiality. While choice and secrecy protect the individual's right to privacy at the collection stage, confidentiality safeguards this right at the point of disclosure. Laws that focus on the inappropriate use of genetic and health information without addressing the act of collecting such information, as is the case with American laws prohibiting genetic discrimination by employers and others, fail adequately to preserve privacy and prevent discrimination. Existing laws that do address the collection of personal information, such as Canada's Personal Information Protection and Electronic Documents Act (PIPEDA), the general and statutory laws of Quebec, and recent Manitoba legislation are insufficiently explicit with respect to the legality of genetic and health information collection by employers.
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 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.009 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".