A Ricardian model of climate change in Canada
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract A comparative static ‘Ricardian’ model is used to establish relationships between climate and agricultural land value in Canada. From these relationships, agricultural costs of climate change scenarios are estimated. This study is motivated partly by evidence of potential agricultural benefits of climate change from a similar analysis of the United States by Mendelsohn, Nordhaus and Shaw, and partly by the void of Canadian studies. Furthermore, it extends the analysis to non‐uniform climate change scenarios. Its finding of a slightly positive upper bound on the agricultural benefits from climate change, within a wide margin of error, is motivation for further analysis. Un modèle ricardien de changement climatique au Canada. L’auteur utilise un modèle statique ricardien classique pour établir des relations entre le climat et la valeur des terres agricoles au Canada. A partir de ces relations, on calibre les coûts agricoles de divers scénarios de changement climatique. Cette étude a pris forme en partie en réaction aux résultats d’une analyse similaire de Mendelsohn, Nordhaus et Shaw aux Etats‐Unis, et en partie en réponse à un manque d’études de ce genre au Canada. Cet article étend les analyses aux scénarios de changements climatiques non‐uniformes. Les résultats suggèrent qu’il existe une sorte de borne positive supérieure aux avantages agricoles du changement climatique, à l’intérieur d’une marge d’erreur assez vaste. Voilà qui encourage à poursuivre les analyses.
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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 | 0.001 | 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 it