Facilitating Innovation Within Genomic Research and New Drug Development
Bibliographic record
Abstract
Research and drug development within the life sciences has increasingly relied on advances in genomics to generate new outcomes and treatments. New technologies such as CRISPR offer tremendous opportunities for new therapeutics and treatments. Not surprisingly, new institutions, policy changes towards intellectual property protection, and firm strategies have arisen to facilitate and govern innovative activity within this emerging sphere. This presenter symposium assembles three studies that provide new and complementary vantage points on the emergence of institutions, policies, and firm strategies for the development and commercialization of genomics-based therapeutics. Open Science or Entrenchment? The Role of Biological Repositories on Inter-Institutional Co-Creation Author: Samantha Zyontz; Boston U. Questrom School of Business Do Scientists Get a Free Pass? Gene Patents and Scientific Research in Biology Author: Sina Khoshsokhan; U. of Colorado, Boulder Shelved Innovation - Evidence from the Pharmaceutical Industry Author: Elisabeth Hofmeister; Max Planck Institute for Innovation and Competition
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.024 |
| Scholarly communication | 0.017 | 0.019 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".