Genetic Discrimination: Information Privacy in Public and Private Sectors
Bibliographic record
Abstract
Deoxyribonucleic acid (DNA), is the information of life. The scientific understanding of genetics and biotechnology has resulted in the increased availability and affordability of genetic testing. Such testing can provide valuable information to help individuals make informed decisions regarding their lifestyle and health care. In today’s era of big data and the internet, if such information finds itself in the wrong hands there can be consequences. Genetic discrimination, the unfair treatment of people due to their genetic makeup, often takes place in the insurance industry and by employers. While there are acts and bills to protect Canadian’s personal information in both the public and private sector, Canada remains the only G-7 country without specific protections against genetic discrimination. With the recent passing of Bill S-201: An Act to Prohibit and Prevent Genetic Discrimination in the Senate, Canada is on the cusp of passing legislation to prohibit the requirement for genetic testing, and/or disclosure of test results, in areas such as the provision of insurance and employment.
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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.023 |
| Scholarly communication | 0.020 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".