The Effect of Attitudinal and Sociodemographic Factors on the Likelihood of Buying Locally Produced Food
Bibliographic record
Abstract
ABSTRACT This study explores the factors associated with Canadian consumers locally produced food purchase intention. Data from an Internet‐based survey of consumers (n = 1,139) was analyzed using a bivariate probit model. The bivariate probit model related attitudinal, behavioral and sociodemographic factors to the intention to purchase fresh and nonfresh locally produced foods. Although sociodemographic characteristics play a limited role in shaping local food purchase intentions, attitudinally based variables have far greater influence. Positive views towards local farmers and agriculture in general, as well as food quality, are positively related to purchase intention. The importance placed on brand‐specific quality is inversely related to the intention to buy local food. Consumers with heightened levels of food involvement, either growing food or preparing most meals from scratch, are more likely to purchase local foods. (EconLit Citations: L660; Q130; Q180)
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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 | 0.007 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".