Public Research Policy for Today's Agricultural Biotech Research Industry
Bibliographic record
Abstract
While the public sector has historically played a very significant role in the direct provision of agricultural research, the appropriate role of government in the future is no longer apparent in a world with intellectual property rights (IPRs) and a concentrated privatized biotech research industry. This study develops a search/imperfect competition framework to examine the public role. The analysis shows that private firms have inadequate incentives to invest in research for varietal improvement relative to the social optimum even with completely enforceable IPRs. A government subsidy on research output can efficiently increase the amount of applied research to the socially optimal point. Government subsidy of the research cost can have the same effect on research and development expenditure. Expanding direct applied public research increases social welfare but cannot achieve a social optimum, as it reduces the already limited incentives for private firms to invest. Finally, in situations where basic research is underprovided, government should address these market failures as part of an optimal research policy. Overall, the analysis suggests that there is a role for public support of applied research, but this role is no longer direct public involvement in applied research where IPRs are well established.
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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.012 | 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".