Exploring the Influence of Quality and Safety on Consumers’ Food Purchase Decisions in Botswana
Bibliographic record
Abstract
The main purpose of this research was to gain some exploratory insights into how important food quality and safety issues were in influencing Botswana customers’ food purchase decisions. A cross-sectional descriptive research design utilising a structured questionnaire was used to collect data from a sample of 150 respondents who reside in Gaborone, Botswana in January 2011. The study found that in defining quality and safety of food, consumers used the same attributes thus resulting in an overlap in definitions of the two concepts. It also revealed that both food quality and safety were considered important by consumers. Consumers also perceived a relationship between quality and safety as they believed the two to be related. The implication from this research is that, consumers in developing countries such as Botswana also have concerns regarding food quality and safety and that the two concepts are important in their daily food choices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".