Bibliographic record
Abstract
We analyze how variations in contractibility affect the design of contracts in the context of biotechnology research agreements.A major concern of firms financing biotechnology research is that the R&D firms might use the funding to subsidize other projects or substitute one project for another.We develop a model based on the property-rights theory of the firm that allows for researchers in the R&D firms to pursue multiple projects.When research activities are nonverifiable, we show that it is optimal for the financing company to obtain the option right to terminate the research agreement while maintaining broad property rights to the terminated project.The option right induces the biotechnology firm researchers not to deviate from the proposed research activities.The contract prevents opportunistic exercise of the termination right by conditioning payments on the termination of the agreement.We test the model empirically using a new data set on 584 biotechnology research agreements.We find that the assignment of termination and broad intellectual property rights to the financing firm occurs in contractually difficult environments in which there is no specifiable lead product candidate.We also analyze how the contractual design varies with the R&D firm's financial constraints and research capacities and with the type of financing firm.The additional empirical results allow us to distinguish the property-rights explanation from alternative stories, based on uncertainty and asymmetric information about the project quality or research abilities.
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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 | 0.045 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| 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".