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Record W2808653426

식품소비성향의 구조 변화 분석

2018· article· ko· W2808653426 on OpenAlexaboutno aff
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Bibliographic record

Venue농업경제연구 · 2018
Typearticle
Languageko
FieldNursing
TopicNutrition, Health and Food Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Consumption (sociology)Food consumptionEconomicsGovernment (linguistics)Agricultural economicsGeographySociology
DOInot available

Abstract

fetched live from OpenAlex

The purpose of this study is to analyze the timing of structural changes in food consumption based on time series analysis. According to the results of Quandt-Andrews Breakpoint test, in the case of propensity of food consumption, the first quarter of 1990, the first quarter of 1999, and the first quarter of 2009 are estimated to be the periods of significant structural changes. In the case of propensity of home food consumption, it is estimated that the second quarter of 1990 is the period when significant structural changes occurred. The analysis of structural changes in this study suggests that food consumption trends are not being held constant over time, but are changing continuously. Therefore, the producers of food and the government that establishes food policy should establish marketing strategy or policy that actively utilizes the changes in consumption trend.

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 score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.035
GPT teacher head0.342
Teacher spread0.307 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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