Bibliographic record
Abstract
Empirical contributions to the debate over the commercialization of the life sciences are as rich in detail as they are diverse in approach and conclusions Citationbased analyses of the genetics literature suggest that patenting undermines knowledge use Survey research meanwhile supports the intuition that university scientists are unhindered daytoday by patent rights Material transfer agreements and other forms of intellectual property contracting are rather what appear to slow progress Other qualitative data imply that our understanding of the role that institutional contexts funding imperatives professional hierarchies and norms of scientific competition play is inadequate The hypothesis motivating many of these investigations the tragedy of the anticommons coupled with the ongoing controversy over gene patents undermining access at the point of patient care has generated litigation against patentholders such as Myriad Genetics Inc opened new areas of empirical inquiry and begun to reveal the complexity of commercializing scientific pursuitsIn addition to summarizing the strengths and limitations of this evolving body of empirical work the contribution I make to the debate over commercialization in the life sciences is twofold First I theorize a novel tradeoff of academic entrepreneurialism I term this tradeoff patent canalyzation and posit that by virtue of participating in the commercialization of their work academic scientists become more insular in terms of who they collaborate with and more entrenched in their chosen line of research inquiry Patent canalyzation theory thus draws attention to potential costs as well as potential benefits associated with patenting that are not captured by the anticommons hypothesis Second I develop a novel methodology to empirically test for patent canalyzation amongst leading scientists in the field of cancer epigenetics Specifically I track 1 coauthoring relationships and 2 the diversity of lines of research inquiry across each scientist's publication record in order to discern which if any intervening patenting events account for significant changes in those two types of variables In a sample of fiftytwo academic scientists there is a negative relationship between applying for a patent and four measures of scientific collaboration and research diversity The duration and magnitude of those negative effects depend upon the frequency with which a scientist applies for patents as well as his or her cumulative years of academic experience and can be moderated by elevated publication output or partially negated by receipt of a patent grant The results support and nuance patent canalyzation theory motivating a series of questions around intellectual property policy academic autonomy and the task of commercializing epigenetic biomarkers currently shared by academic and company labs
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.301 | 0.063 |
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".