Unraveling Elusive Boundaries: A Comprehensive Framework for Assessing Local Food Consumption Patterns in Nova Scotia, Canada
Bibliographic record
Abstract
Promoting local food consumption for economic growth is a priority; however, defining "local" remains challenging. In Nova Scotia, Canada, this pioneering research establishes a comprehensive framework for assessing local food consumption. Employing three data collection methods, our study reveals that, on average, Nova Scotians allocate 31.2% of their food expenditures to locally sourced products, excluding restaurant and take-out spending, as per the provincial guidelines. The participants estimated that, in the previous year, 37.6% of their spending was on local food; this figure was derived from the most effective method among the three. However, the figure was potentially influenced by participant perspective and was prone to overestimation. To enhance accuracy, we propose methodological enhancements. Despite the limitations, the 31.2% baseline offers a substantial foundation for understanding local food patterns in Nova Scotia. It serves as a replicable benchmark for future investigations and guides researchers with similar objectives, thereby establishing a robust research platform.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".