Does nutritional labeling increase healthy eating fallacy? An exploration into young Indian’s purchase behavior
Bibliographic record
Abstract
The paper examines young consumers' responses towards nutritional labeling, it's information content and the importance of the functional characteristics of these labels as perceived by young consumers in making informed purchase decisions through personal interviews of 220 respondents using a structured questionnaire. Factor analysis was performed to identify the underlying dimensions among a set of nutritional labeling parameters using principal component analysis. Based on factor analysis, ten factors emerged. Regression analysis and t test indicated that, out the ten factors, only three factors namely 'Nutritional Belief', 'Storage instruction & Information overload', and 'Exercise & Nutrition' were significant. These factors were mainly inclined outside the purview of nutritional labeling purchase influence .It was thus concluded that nutritional labeling had less influence in purchase decisions as far as young consumers were considered. Findings of the study give practical insights on food labeling issues for the food processors and policy makers.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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".