Who Governs Producer Controlled Research Organizations in the Agricultural sector, and Why?
Bibliographic record
Abstract
Producer controlled research organizations (PCR)s) are charged with the task of investing hundreds of millions of dollars into research and development and promotion projects. In a series of interviews with the managers and directors of some of the key PCROs in Australia, the US, and Canada we observed that PCROs do not tend to separate management and oversight tasks. The producers elected directors of these organizations are involved in management decisions. This observed practice is in contrast with most of the theories and empirical studies focusing on the governance structure of non-profit (NP) and for-profit (FP) organizations (Brown & Guo, 2010; Fama & Jensen, 1983; LeRoux & Langer, 2016 ). Based on information gained from the interviews, observable characteristics of PCROs explained in the literature, and agency theory this paper develops a theoretical model to describe the unusual task assignment in the PCROs. The theoretical model suggests that because of the long investment horizons in the PCROs, the compensation of management teams based on their contributions to return on investments is not feasible. Therefore, the PCROs have to reward their executives on the basis of a measure of efforts exerted. Hence, the directors involvement reduces the volatility of managers compensation. Acknowledgement : we would like to thank Bill Kerr, Eric Micheels, Joel Bruneau, Murray Fulton and Brian Olsen for their comments and thoughts. We would also like to thank Saskatchewan Pulse Growers and Saskatchewan wheat development commission for their financial support.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".