Habits, perceptions and attitudes of Quebec Metropolitan Community’s (QMC) consumers toward locally produced foods
Bibliographic record
Abstract
Integration phase (Objective C)Interpretation of the results using connected data analysis 11 ; Decide how the qualitative results explain the quantitative results and draw inferences 11 ; Mixed-methods question : In what ways commonly held beliefs regarding local food consumption of a sample of QMC consumers help to understand their actual behavior toward local food consumption and the quality of their diet? Qualitative phase (Objective B)Recruitment : (n of participants expected = 80)Stratified purposeful sample drawn from the quantitative phase that may help to explain obtained results. Data collection :Focus groups (groups = 8) Homogeneous segmentation groups of consumers according to their "locavore score" (high vs. low) and the degree of accessibility to SFSC in their neighbourhood (more vs less); Using a standardized semi-structured interview guide based on TPB's constructs 5 and inspired by Krueger's questioning route 6 ; TPB in its original form or with added constructs as been used by many authors to investigate the psychological and contextual factors that drive consumers' behaviour toward local food consumption 7-10 .
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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 | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".