Agricultural biotechnology and industry structure.
Bibliographic record
Abstract
In the last ten years the seed and pesticide industries have undergone a substantial number of structural changes. These changes are due to a number of factors, some of which are common to all industries and some of which are specifically tied to the biotechnology that is increasingly important in the seed and chemical industries. The focus of this paper is on these latter linkages. The horizontal mergers and acquisitions can be linked to R&D costs, economies of scale and scope created by intellectual property rights, and to regulatory costs, while the increased vertical linkages are connected to product complementarity and to the difficulty in enforcing certain types of intellectual property. In other cases, the rise of better defined intellectual property rights has been a factor in the joint ventures and strategic alliances that have occurred. The pricing behavior of the large firms in the seed and chemical industries appears to be strategic in nature, with pricing being influenced by competition from other products and the value created by their products. There is substantial evidence of price discrimination, whether it is in the form of TUAs, differential pricing, or tied sales. The major impact of this strategic pricing is not on the total economic surplus created as a result of R&D, but rather on the distribution of this surplus.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.049 | 0.009 |
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".