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Record W3014094017 · doi:10.1111/1477-9552.12372

Farm Size, Technology Adoption and Agricultural Trade Reform: Evidence from Canada

2020· article· en· W3014094017 on OpenAlexaboutno aff
W. Mark Brown, Shon Ferguson, Crina Viju‐Miljusevic

Bibliographic record

VenueJournal of Agricultural Economics · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
FundersJan Wallanders och Tom Hedelius Stiftelse samt Tore Browaldhs Stiftelse
KeywordsCanolaAgricultureSubsidyAgricultural economicsDistribution (mathematics)Shock (circulatory)EconomicsValue (mathematics)Production (economics)CensusUnit (ring theory)GeographyAgronomyMarket economy

Abstract

fetched live from OpenAlex

Abstract Using detailed census data covering over 30,000 farms in Alberta, Saskatchewan and Manitoba, Canada, we document the vast and increasing farm size heterogeneity, and analyse the role of farm size in adapting to the removal of an export subsidy in 1995. Consistent with the Alchian‐Allen hypothesis, the increase in per‐unit trade costs due to the reform was associated with farms of all sizes shifting their production of crops from low value wheat to higher value canola. We find that switching to new labour‐saving tillage technologies and away from summerfallow in response to the large negative shock to grain prices caused by the reform varied across the farm size distribution. We develop a theory of heterogenous farms and technology adoption that can explain our findings.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.116

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.183
Teacher spread0.166 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2020
Admission routes1
Has abstractyes

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