Bibliographic record
Abstract
Abstract Agricultural policy frameworks such as Growing Forward are intended to enhance the productivity and competitiveness of the Canadian agricultural sector and to stabilize farm income. This paper examines the relationship between production efficiency and government program payments. First, we find evidence of heterogeneity in production efficiency across farms. Second, we find a negative correlation between production efficiency and the share and level of program payments. The result of this study underscores the importance of understanding the link between technical inefficiency and government payments. Les cadres stratégiques agricoles comme Cultivons l'avenir existent pour favoriser la productivité et la compétitivité du secteur agricole canadien et pour stabiliser les revenus des exploitations agricoles. Cet article examine la relation entre l'efficience de la production et les programmes gouvernementaux de paiements. En premier lieu, nous avons trouvé des preuves d'hétérogénéité dans l'efficience de la production dans toutes les exploitations agricoles. Ensuite, nous avons identifié une corrélation négative entre la production efficiente,et la part et niveau des programmes de paiements. Le résultat de cette étude met en relief l'importance de comprendre le lien entre les inefficiences techniques et les paiements gouvernementaux.
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.005 | 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 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".