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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 OpenAlex

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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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