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Record W2605791014

The Farm Share in Canada from 1997 to 2010: Identifying Trends in Value Distribution Along the Agri-Food Supply Chain

2014· dissertation· en· W2605791014 on OpenAlexaboutno aff
Jessica Kelly

Bibliographic record

VenueThe Atrium (University of Guelph) · 2014
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Economics and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Distribution (mathematics)Supply chainAgricultural economicsFood supplyChain (unit)Agricultural scienceValue chainBusinessGeographyEnvironmental scienceEconomicsMarketingStatisticsMathematics
DOInot available

Abstract

fetched live from OpenAlex

The farm share is the average portion from each dollar of expenditure on food that is received by farmers for their agricultural commodities. Variation in the farm share could reflect regular fluctuations in margins and asymmetric supply elasticities or be indicative of larger sustained trends such as the existence of market power or changing customer preferences. This thesis is an investigation of the Canadian farm share from 1997 to 2010, using input-output techniques that were developed by the Economic Research Service. Results show that the Canadian farm share decreased about 0.20% annually over this period, similar to results for the United States. Decomposition of these farm share results reveals that the farm share measure varies greatly across food commodities. Simulations show that rises in the value-added cost of corn, energy, and farm labour all result in an increase to the farm share.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.037
Threshold uncertainty score0.266

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.009
Science and technology studies0.0010.000
Scholarly communication0.0020.001
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 head0.015
GPT teacher head0.196
Teacher spread0.181 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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