Promoting local food products for sustainability: Developing a taxonomy of best practices
Bibliographic record
Abstract
Abstract The number of practices promoting local food have been rapidly growing in recent years, mainly in response to the increasing interest from consumers and their sustainability benefits. The objective of this scoping review is to present a taxonomy of the most promising promotion practices in this domain. A targeted search within a database specialized in storing newspaper articles resulted in the identification of 78 documents that were published prior to COVID‐19 pandemic. Their analysis led to the development of a taxonomy containing four major categories regrouping 14 types of initiatives to promote local food products. The review also highlighted the principal strengths of these initiatives (e.g., facilitating access to local food products, promoting social diversity, integration, and solidarity), as well as some obstacles (e.g., funding, infrastructure, and volunteer recruitment). The taxonomy presented in this review can be used to guide the analysis of existing or the development of new local food promotion initiatives, thus encouraging sustainability in this domain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.037 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.043 | 0.038 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".