Explanatory Analysis of Factors Influencing the Support for Sustainable Food Production and Distribution Systems: Results from a Rural Canadian Community
Bibliographic record
Abstract
Investigating the viability of alternative food networks (AFNs) is more important than before because of the disruptions in global supply chains and evolving resident composition in different regions. In this regard, this paper reports on findings of a project aimed at identifying factors influencing support for local, sustainable food production, and distribution systems. In the first phase, local residents and international students in Cape Breton, Canada, were surveyed prior to the onset of the coronavirus disease 2019 (COVID-19) pandemic to assess their attitudes and values relative to shopping at farmers markets and buying local. In the second phase, mid-pandemic, text mining of Twitter data was used to gauge sentiments related to these same activities. The results of our explanatory analysis suggest that the top two factors influencing decisions to buy local farm products were food attributes and supporting community economic development. In contrast to previous studies, we included an alternate sample group, namely, international students, and explored the relevance of the social aspect of buying local, e.g., meeting the farmer. Among our findings from the application of a logistics regression model to our survey data (N = 125) is the suggestion that the senior non-international student residents of the Cape Breton Island were more probable to be in the category of consumers whose perception of an authentic buy-local experience was limited to distribution channels that allowed for the social aspect of buying local, e.g., meeting the farmer.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".