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Record W2293544666 · doi:10.5539/jas.v8n4p50

Substitution in Consumer Demand for Coffee Product Categories in Japan

2016· article· en· W2293544666 on OpenAlexvenueno aff
Michael Fesseha Yohannes, Toshinobu Matsuda, Naoko Sato

Bibliographic record

VenueJournal of Agricultural Science · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomics of Agriculture and Food Markets
Canadian institutionsnot available
Fundersnot available
KeywordsAlmost ideal demand systemAgricultural economicsProduct (mathematics)Coffee beanConsumption (sociology)Consumer Expenditure SurveyConsumer demandEconomicsPrice elasticity of demandAgricultural scienceBusinessFood scienceMathematicsProduction (economics)MicroeconomicsEnvironmental science

Abstract

fetched live from OpenAlex

This paper estimates substitution in consumer demand for coffee product categories in Japan using the linear approximate quadratic almost ideal demand system model (LA/QUAIDS). Three expenditure shares and demand equations for coffee beans and powder (beans/powder), canned and bottled coffee (canned/bottled) and coffee drunk at coffee shops (coffee shops) are estimated for two or more person households in forty-nine cities for the period January 2000 through February 2015. The expenditure elasticity estimates indicate that coffee shops are luxury goods while beans/powder and canned/bottled coffee are necessities in the Japanese household. The demographic effects show that persons over the age of 65 and people who earn more consume coffee at coffee shops. Moreover, seasonal effects show demand for canned and bottled coffee as mostly positive while it is mostly negative for coffee drunk at coffee shops in most of the months. The findings of this study indicate that coffee product categories in the Japanese market are substitutes for one another, which is consistent with the reality of coffee consumption in Japan.

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)0.002
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 score0.124
Threshold uncertainty score0.246

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.020
GPT teacher head0.214
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 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

Citations5
Published2016
Admission routes1
Has abstractyes

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