An Agent‐Based Simulation Model of Structural Change in Canadian Prairie Agriculture, 1960–2000
Bibliographic record
Abstract
Agent‐based simulation modeling (or ABM) is used to examine the evolution of farm size and financial structure in Canadian prairie agriculture over the period 1960–2000. Individual farm agent interaction and dynamics in this model occur through land ownership and leasing markets. A base scenario is developed and the model is validated against actual data from a typical Saskatchewan farm region. Subsequently, we simulate counterfactual policy scenarios applicable to farms in this region. Overall, we view the contribution of this paper as twofold—first, it represents a “proof of concept” of ABM's ability to simulate farming on a medium to large scale. Second, the model allows us to examine the contributions of entrepreneurship, factor endowment, and historical government agricultural support payments on the evolution of Canadian prairie farm structure. Dans le présent article, nous avons utilisé un modèle multi‐agent pour analyser l'évolution de la taille et de la structure financière de fermes dans les Prairies canadiennes, de 1960 à 2000. Dans ce modèle, les agents interagissent dans le temps par le biais des marchés de la propriété foncière et du bail foncier. Nous avons élaboré un scénario de référence et nous avons validé le modèle à partir de données réelles tirées d'une région agricole typique de la Saskatchewan. Ensuite, nous avons simulé des scénarios contrefactuels de politiques applicables à des fermes de cette région. Le présent article poursuivait un double objectif. Le présent article est à double volet. Premièrement, il comprend une démonstration de faisabilité de la capacité du modèle multiagent à simuler l'agriculture de moyenne à grande échelle. Deuxièmement, le modèle a permis d'examiner l'apport de l'entrepreneuriat, de la dotation en facteurs de production et des paiements de soutien agricole sur l'évolution de la structure des fermes dans les Prairies canadiennes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".