Open science versus commercialization: a modern research conflict?
Bibliographic record
Abstract
BACKGROUND: Efforts to improve research outcomes have resulted in genomic researchers being confronted with complex and seemingly contradictory instructions about how to perform their tasks. Over the past decade, there has been increasing pressure on university researchers to commercialize their work. Concurrently, they are encouraged to collaborate, share data and disseminate new knowledge quickly (that is, to adopt an open science model) in order to foster scientific progress, meet humanitarian goals, and to maximize the impact of their research. DISCUSSION: We present selected guidelines from three countries (Canada, United States, and United Kingdom) situated at the forefront of genomics to illustrate this potential policy conflict. Examining the innovation ecosystem and the messages conveyed by the different policies surveyed, we further investigate the inconsistencies between open science and commercialization policies. SUMMARY: Commercialization and open science are not necessarily irreconcilable and could instead be envisioned as complementary elements of a more holistic innovation framework. Given the exploratory nature of our study, we wish to point out the need to gather additional evidence on the coexistence of open science and commercialization policies and on its impact, both positive and negative, on genomics academic research.
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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 | 0.195 | 0.215 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.011 | 0.083 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.013 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".