Bibliographic record
Abstract
To open this address, I would like to advocate for membership in the Canadian Agricultural Economics Society (CAES). The fact that applied economics offers theory and methods that help us address topics as diverse as the Canadian grain value chain and the economics of species at risk speaks well for the future of our discipline. There is a vast array of work for us to do. Membership in the CAES offers an excellent link to the most up‐to‐date research in this area through our journal and conferences. Every society I attend inspires me to examine my own research and look at problems in new ways using new tools I learned from presentations made by the members of CAES. The main message of my address is to promote the application of Game Theory strategies as a way to understand behavior in the grain value chain. These tools are already being applied in areas as different as optimizing tradable systems of environmental goods and assessing competitive behavior in beef packing. Tout d'abord, je tiens à mentionner que j'appuie l'adhésion à la Société canadienne d'agroéconomie (SCAE). Le fait que l’économie appliquée offre la théorie et les méthodes qui nous aident à examiner des sujets aussi variés que la chaîne de valeur des grains du Canada et l’économie des espèces en péril augure bien pour l'avenir de notre discipline. La diversité du travail à accomplir est immense. Être membre de la SCAE procure un lien privilégié à la recherche de pointe grâce à notre Revue et à nos conférences. Aujourd'hui, mon message vise principalement à promouvoir l'application des stratégies de la théorie des jeux pour comprendre le comportement au sein de la chaîne de valeur des grains. Certains domaines, tels que l'optimisation des systèmes d’échange des biens environnementaux et l’évaluation du comportement concurrentiel dans le secteur du conditionnement du bœuf, utilisent déjà ces outils. La panoplie d'outils à notre disposition est mise en valeur dans notre Revue et lors de nos congrès. Tous les congrès auxquels j'assiste me motivent à examiner ma propre recherche et à analyser les problèmes sous un angle différent grâce aux nouveaux outils mis au point et présentés par les membres de la SCAE.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".