Managing Innovation in the Market for Ideas: Open Access, Patent Enforcement and Creativity.
Bibliographic record
Abstract
Licensing, sale of patents and other knowledge dissemination strategies - the ‘market for ideas’ - are key sources of R &D incentives. The Symposium offers an overview of recent research on the “market for ideas” emphasizing the role of patent enforcement, open access and patent protection in strategic decisions related to technology transactions, employee retention and creativity.Trading and Enforcing Patent RightsPresenter: Alberto Galasso; U. of TorontoPresenter: Carlos J Serrano; U. of TorontoPresenter: Mark Schankerman; London School of EconomicsKeeping Distance: Patent Enforcement and the Relocation of Knowledge WorkersPresenter: Martin Ganco; U. of MinnesotaPresenter: Rosemarie Ziedonis; U. of OregonPatent Pools, Thickets, and Open Source Software Entry by Start-Up FirmsPresenter: Wen Wen; Georgia Institute of TechnologyPresenter: Marco Ceccagnoli; Georgia Institute of TechnologyPresenter: Chris Forman; Georgia Institute of TechnologyDoes Copyright Encourage Creativity? Empirical Evidence from the 1711 Statute of AnnePresenter: Megan MacGarvie; Boston U.Presenter: Petra Moser; Stanford U.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".