Proprietary Interests and Collaboration in Stem Cell Science: Avoiding Anticommons, Countering Canalyzation
Bibliographic record
Abstract
In this chapter I explore how proprietary interests and commercialization norms can impede collaboration in stem cell science. I begin by outlining three layers of property in stem cell science—stem cell data, stem cell materials, and stem cell patenting—and explain how they are intertwined in practice. I then present two stem cell research initiatives, the Cancer Stem Cell Consortium (CSCC) and Stem Cells for Safer Medicines (SC4SM). Using two conceptual frames, the “tragedy of the anticommons” and “patent canalyzation,” I analyze the extent to which the CSCC and SC4SM appear to address proprietary or commercialization-related impediments to collaboration. Whereas the anticommons frame, and empirical methodologies it has spawned to date, tends to capture costs imposed upon the scientific fields as a whole, patent canalyzation focuses on the individual scientist, hypothesizing that patenting and other commercialization behaviours may (re)constitute the scientific self. The chapter concludes by highlighting three intellectual property-related best practices intended to facilitate collaboration in stem cell science.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".