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Record W2547037833

Conceptualising of quantities by low-income consumers to guide recipe format

2013· article· en· W2547037833 on OpenAlexaboutno aff
Sara S Duvenage, Wilna Oldewage‐Theron, Abdulkadir Egal

Bibliographic record

VenueInternational journal of home economics · 2013
Typearticle
Languageen
FieldHealth Professions
TopicFood Security and Health in Diverse Populations
Canadian institutionsnot available
Fundersnot available
KeywordsRecipeQuarter (Canadian coin)Table (database)Computer scienceMetric (unit)EngineeringOperations managementFood scienceGeographyDatabase
DOInot available

Abstract

fetched live from OpenAlex

Availability of soy was improved through a home-gardening project in rural Qwa-Qwa. Soy-containing recipes were developed and need to be published. The challenge was to identify guidelines, suiting the target consumers (n=91), for measuring units and for lay-out of recipes. Trained fieldworkers, fluent in the indigenous language, conducted personal interviews. Spoons and cups in general household use were employed for measuring purposes, using levelled measures. Units reported as cups and spoons were much preferred to metric units. Perceptions of quantities for cups were reported correctly for full (89%), half (78%), one third (3%) and one quarter (31%) cup units. For spoons, perceptions were reported correctly for full (97%), half (90%) and quarter (77%) units. Numeric format was indicated as being clearer than visual units for indicating quantities, but most preferred a combination of both methods for all quantities. To meet consumer perceptions, quantities will be specified as full or half cups, and smaller units as tablespoons, teaspoons and units thereof. To allow for the use of commercial measuring equipment and the physical limitations of recipe book format, both forms of measurement will be employed. However, a comparison table will be compiled, including the visual format, to indicate different standardised options for measuring equal quantities.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.301
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.080
GPT teacher head0.425
Teacher spread0.346 · 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 designNot applicable
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
Published2013
Admission routes1
Has abstractyes

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