Constructing narratives of heroism and villainy: case study of Myriad's BRACAnalysis® compared to Genentech's Herceptin®
Bibliographic record
Abstract
BACKGROUND: The development of Herceptin(®) is welcomed as a major advance in breast cancer treatment, while Myriad's development of BRACAnalysis(®) is a widely used diagnostic. However useful and successful this product is, its presence in the public eye is tainted by predominantly negative press about gene patenting and business practices. DISCUSSION: While retrospection invites a sharp contrast between Genentech's triumphal narrative of scientific achievement and Myriad's public image as a controversial monopolist, a comparative history of these companies' products reveals two striking consistencies: patents and public discontent. Despite these similarities, time has reduced the narrative to that of hero versus villain: Genentech is lauded - at least for the final outcome of the Herceptin(®) story - as a corporate good citizen, Myriad as a ruthless mercenary. Since patents undergird both products yet the narratives are so different, the stories raise the question: why have patents taken the fall as the scapegoat in current biotechnology policy debate? SUMMARY: A widely publicized lawsuit and accompanying bad press have cast Myriad as a villain in the evolving narrative of biotechnology. While the lawsuit suggests that this villainy is attributable to Myriad's intellectual property, we suggest through a comparative case study that, at least in the Myriad case, it is not simply about the patents but also other business strategies the company chose to pursue. Patents were a necessary but not sufficient cause of controversy.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.041 | 0.029 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".