Market Impacts of Technological Change in Canadian Agriculture
Bibliographic record
Abstract
Market impacts of technological change in Canadian agriculture are measured within a computable general equilibrium framework using 2001 input‐output data with agriculture disaggregated to six sectors and 13 commodities. Technological change is modeled as productivity rises in the use of intermediate inputs and of primary factors. Impacts on output, intermediate use of output, foreign trade, final consumption, returns to primary factors, and relative prices are calculated for primary agricultural commodities and processed food products. Impacts are summarized as three general outcomes. First, supply managed sectors adjust to technological change differently than other agricultural sectors. In the former, quota rents increase while in the latter, outputs, exports, and final consumption increase along with declines of relative supply prices. Second, large relative price declines for other commodities lead to consumer gains. Third, producer gains increase when the international competitiveness of agriculture increases. Finally, we compare the differential impact of technological change with and without supply management . L'impact que le changement technologique au sein de l’agriculture canadienne a sur le marché est évaluéà l’aide d’un modèle d’équilibre général calculable (EGC) qui utilise des données entrées‐sorties de 2001 pour six secteurs agricoles et treize produits de base. Le changement technologique est modélisé en termes de hausses de productivité dans l’utilisation d’intrants intermédiaires et primaires. L'impact sur les extrants, l’utilisation intermédiaire d’extrants, le commerce extérieur, la consommation finale, les rendements des intrants primaires et les prix relatifs sont calculés pour les principaux produits de base agricoles et produits alimentaires transformés. L'impact est classé en trois catégories de résultats. Premièrement, les secteurs soumis à la gestion de l’offre s’adaptent différemment des autres secteurs au changement technologique. Dans le premier cas, les rentes de contingentement augmentent tandis que dans le second, les extrants, les exportations et la consommation finale augmentent et les prix relatifs de l’offre diminuent. Deuxièmement, les chutes importantes du prix relatif d’autres produits de base entraînent des avantages pour le consommateur. Troisièmement, les gains du producteur augmentent lorsque la compétitivité de l’agriculture sur la scène internationale augmente. Finalement, nous avons comparé l’impact différentiel du changement technologique dans les secteurs soumis et non soumis à la gestion de l’offre .
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".