The Gene Patenting Controversy
Bibliographic record
Abstract
Genes are found in every living thing. They make us who we are. In the modern age of rapid technological advances, it is common for new discoveries to be patented for profit. A question arises. Does finding and isolating a human gene make it patentable by a company? This question has spurred much controversy over the last few years. Last year, a medical company called Myriad Genetics tried to patent the human gene that is responsible for breast cancer. To further understand the opinions of the public with regards to the issue of gene patenting, we conducted a study in one high school in Mississauga. Our results showed that the majority of teenagers are against gene patenting. However, we learned that teens knew very little about this issue. This motivated us to take action by making two informational videos on gene patenting to spread awareness among our peers and the wider community. In addition, we reached out to a group called Breast Cancer Association (BCA) by writing a letter to express our solidarity. We received a ‘thank you’ reply for BCA shortly after the U.S. Supreme
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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.026 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.028 | 0.024 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".