Understanding Open Versus Proprietary Research and Innovation: A Case Study of Canada's Pharmaceutical Sector
Bibliographic record
Abstract
With decreasing public funding for scientific research and innovation (R&I) in Canada, the onus has fallen on public research institutions strategically partner with industry to ensure that research generates innovative socio-economic gains. As a result, R&D has become more prescribed and more restricted, as private contracts and other proprietary intellectual property (IP) mechanisms regulate and often limit avenues of inquiry. This push towards commercialization has extended upstream into the process of research itself, and is not limited solely to product development (Mirowski and Van Horn, 2005). \n \nIn response to the restraints on R&I imposed by commercialization and proprietary IP measures, concepts of open science and innovation have become increasingly prominent, particularly in discussions of pharmaceutical development. The push towards openness in R&I has offered a potential solution to navigating through complex networks of proprietary IP licenses and patents, primarily by releasing project data into the public domain and ensuring broad user access, expanding participation in R&I, and reducing commercial barriers (Gitter, 2013; Feldman & Nelson, 2008). While open science initiatives offer low entry costs and increased methodological transparency, there is significant debate within the STS and innovation studies literature regarding the role of open and proprietary IP in R&I. While some, such as Lezuan and Montgomery (2015), argue proprietary mechanisms are necessary for collaboration and provide incentives for investing in research, others, such as Mirowski (2011), highlight the aforementioned roadblocks to innovation and collaboration brought about proprietary IP. In both cases, open and proprietary mechanisms are often presented as dichotomous and incompatible. \n \nThis dissertation builds on the argument that, contrary to this dichotomy presented in current STS scholarship, these open and proprietary mechanisms may be complimentary at particular stages of R&I. I extend my focus to intermediary organizations established to facilitate the translation of basic research into marketable pharmaceutical products, in addition to public research institutes, small- to medium-sized private pharmaceutical firms, and incubator labs in Toronto. In doing so, this research aims to unpack how these mechanisms operate in the R&I process, as well as their role in facilitating or hindering collaboration and pharmaceutical R&I more broadly.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".