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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".