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Record W7144090153 · doi:10.24516/00000222

Cooking habits and skills among college students majoring in Food and Nutrition and Home Economics Education: comparing cooking habits and skills before and after taking cooking practices

2009· article· ja· W7144090153 on OpenAlexaboutno aff
ミツヨ ホリ, マドカ ヒラシマ, 由香 磯部, ヒロコ ナガノ, Mitsuyo Hori, Madoka Hirashima, Yuka Isobe, Hiroko NAGANO

Bibliographic record

VenueInstitutional Repositories DataBase (IRDB) · 2009
Typearticle
Languageja
FieldHealth Professions
TopicHealth, Technology, Consumer Behavior
Canadian institutionsnot available
Fundersnot available
KeywordsFamily and consumer scienceFish <Actinopterygii>Nutrition EducationFood preparationQuarter (Canadian coin)Food habitsCooking methods

Abstract

fetched live from OpenAlex

A questionnaire survey was conducted among college students majoring in Food and Nutrition and Home Economics Education with the purpose of investigating how much their understanding about cooking habits and skills had improved with a semester cooking practices. Comparison of the results before and after the cooking practices showed the following educational effects: students were cooking more often and more students had their specialties than before; the number of students who did not know how to make soup stock other than instant one dropped and students acquired Japanese traditional method to make soup stock; the number of students who could prepare fish increased while the number of those who could not decreased; as to fish preparation, students learned the words zeigo (hard scales at the joint of the tail of mackerels) and chiai (dark muscle); a high percentage of students acquired basic techniques to cut food into rounds (wagiri), small pieces from its edge (koguchigiri) and quarter rounds (ichohgiri).

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0030.001
Scholarly communication0.0010.003
Open science0.0000.001
Research integrity0.0010.002
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 head0.026
GPT teacher head0.360
Teacher spread0.334 · 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 teacher head, not a consensus.

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
Published2009
Admission routes1
Has abstractyes

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