Conceptualising of quantities by low-income consumers to guide recipe format
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".