Strategic Patenting, Innovation and Competition in Pharmaceuticals
Bibliographic record
Abstract
The pharmaceutical industry is one of the most significant sectors in the economy -- both in terms of economic impact and welfare implications due to health outcomes. Further, unique attributes of the industry and available data allow for robust empirical studies of strategy and innovation questions that are difficult to observe in many settings. This symposium showcases papers that study the implications of pharmaceutical M&A and IPOs on drug pricing and innovation as well as the link between competition and strategic patenting of pharmaceutical firms. Mergers that Matter: The Impact of M&A Activity in Prescription Drug Markets Author: Josh Feng; U. of Utah, David Eccles School of Business Author: Thomas Hwang; Harvard Medical School Author: Yunjuan Liu; U. of North Carolina, Chapel Hill Author: Luca Maini; U. of North Carolina, Chapel Hill Does going public affect pharmaceutical innovation? Evidence from clinical trials Author: Jennifer Kao; UCLA Anderson School of Management Author: Charu Gupta; UCLA Anderson School of Management Marketing Authorization and Strategic Patenting: Evidence from Pharmaceuticals Author: Dennis Byrski; Max Planck Institute for Innovation and Competition Author: Lucy Xiaolu Wang; Assistant Professor at UMass Amherst Valuing Pharmaceutical Patent Thickets Author: John McKeon; Boston U. Questrom School of Business Author: Felix Poege; Bocconi U. Author: Tim Simcoe; Boston U.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".