Use of Caswell’s classification on food quality attributes to assess consumer perceptions towards fresh milk in tetra-packed containers
Bibliographic record
Abstract
This study assesses, using the Caswell’s classification on food quality attributes (1998), what quality attributes that consumers consider most important in their decision to purchase fresh milk stored in tetra-pack containers, and the impact of a number of socio-economic characteristics of consumers on this behavior. A consumer survey was carried out (n=664) in the Gampaha district from April to May in 2005, and data pertaining to 100 who consume it with the highest frequency (i.e. 3.43 packs/week) were considered for the empirical analysis. The results based on two indices, namely the “Mean Score of Quality Attribute” (MSQA) and the “Food Quality Responsive Index” (FQRI) suggest that attributes such as purity, appearance, size, convenience, and informational labeling from “value” and “package” subsets were the most important. It also shows that consumers did not judge that tetra-packs enhance attributed included in the “food safety” and “nutrition” subsets to a larger extent. The statistic outcome based on Ordered Logistic regression techniques, where the values of FQRI were used to develop four dependent variables, shows that factors such as age, marital status, sex, and level of education and income of a consumer have a significant impact on this behavior. The results suggest that the market can work effectively on promoting the sales of fresh milk by enhancing its quality in terms of value and packaging attributes, while the government should take into account of regulating the attributes of food safety and nutrition through appropriate food standards.
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 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".