Modeling and Measuring Productivity in the Agri‐Food Sector: Trends, Causes and Effects
Bibliographic record
Abstract
This article overviews recent trends in modeling and measuring productivity patterns, and in distinguishing their determinants and implications, for the agri‐food sector. Theoretical methodologies as well as empirical implementation and results are discussed, with a view toward identifying those with potential for facilitating understanding of productivity measures, and ultimately using them for policy guidance. Productivity growth evidence for the food systems of the U.S., Canada and the U.K. is summarized, and recent studies distinguishing underlying causes of production structure patterns and linking them with market‐structure patterns are reviewed, as a basis for assessing the key messages from and trends in this literature. L'auteurfait un survol de révolution récente dans les domaines de la modélisation et de la mesure des courbes de productivité ainsi que de la caractérisation de leurs determinants et de leurs significations pour le secteur agroalimentaire. Il passe en revue les méthodes théoriques aussi bien que les applications empiriques et leurs résultats afin d'en dégager ceux qui pourraient faciliter la comprehension des mesures de la productivité et qui, éventuellement, pourraient servir de guide awe décideurs. L'auteur analyse les signes de croissance de la productivité des filières agroalimentaires observés aux Etats‐Unis, au Canada et au Royaume‐Uni. Enfln il examine les études récentes sur les causes sous‐jacentes des évolutions des structures de production et sur leurs liens avec l'évolution des structures de marché, dans le but d'en dégager les messages et les tendances dés.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".