The Effect of Culture on Food Consumption; a Case of Special Religious Days in Turkey
Bibliographic record
Abstract
Cultural beliefs and traditions influence the consumption and the preparation of certain foods especially in special times in different cultures. Religion and traditions in different cultures lead to restrictions of some food from the diet. Culture and religion influence food consumption patterns. This paper documents how culture, religion and traditional knowledge impacts the food purchasing behavior and food choices. Most people in Turkey are Muslims and traditionally, their food consumption is influenced by the culture especially in Ramadan which involves religious fasting days and Ramadan is a time to purify the soul, refocus attention on God, and practice self-discipline and sacrifice. “Şeker Bayramı” which symbolizes the end of the fasting, is the first day of Shawwal, and it is the 10th month of the Islamic calendar period in which people treat their guests to sweets and traditional desserts. “Kurban Bayramı” commemorates Ibrahim’s willingness to sacrifice his son Ishmael to show his faithfulness to Allah takes place 70 days after the end of Ramadan and during these days the meat of the slaughtered animal is shared with the poor and neighbors. The purpose of this paper is to investigate the role of Islamic beliefs and culture in moderating consumer’s attitudes, purchase intentions and consumption of food during these special days. A questionnaire was administered online among 297 participants and the responses were collated with factor analysis, independent sample T-test and ANOVA tests. The empirical research indicates variable food consumption practices during the Muslim festivals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".