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Record W4238098234 · doi:10.1215/00021482-81.4.493

The Far-from-Dry Debates: Dry Farming on the Canadian Prairies and the American Great Plains

2007· article· en· W4238098234 on OpenAlexaboutno aff
Peter A. Russell

Bibliographic record

VenueAgricultural History · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureSettlement (finance)Dry landGeographyScale (ratio)Agricultural economicsAgroforestryArchaeologyAgronomyEnvironmental scienceEconomicsCartography

Abstract

fetched live from OpenAlex

Abstract The absence of dry farming techniques is the key element that many Canadian scholars use to explain the decade of delay in the agricultural development of the prairie region after the completion of the Canadian Pacific Railway in 1885. They have assumed that American farmers had developed an unproblematic set of techniques, the presence of which was essential before large-scale European settlement of the Prairies could begin. American historians of Great Plains agriculture present a far different picture of those American farmers. Dry farming in the United States did not designate an agreed-upon set of techniques, but a lively field of debate that remained unresolved for decades. Summer fallow comprised the essential practice on the driest Canadian Prairies for the conservation of moisture. Americans neither pioneered nor promoted it first; summer fallow only became general practice on the northern Great Plains after Canadian farmers had demonstrated how it could be practicably done. The flow of this agricultural innovation turns out to have been the opposite of what most Canadian scholars had assumed.

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)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.110
Threshold uncertainty score0.795

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0510.036
Scholarly communication0.0070.003
Open science0.0010.004
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0060.000

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 head0.011
GPT teacher head0.206
Teacher spread0.194 · 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 designQualitative
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

Citations1
Published2007
Admission routes1
Has abstractyes

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