Short food supply chains, labor productivity and fair earnings: an impossible equation?
Bibliographic record
Abstract
Abstract Given the limited number of middlemen in short food supply chains, producers marketing through such channels must carry out various tasks associated with production, processing (if applicable) and marketing. Since productivity increases with specialization, it is presumably difficult for such producers to achieve high levels of labor productivity in each of the tasks they perform (in terms of organizing the work and controlling for costs). This study reports on the results of a detailed analysis of the apparent labor productivity in each activity segment (production, processing and distribution) on farms in Quebec (Canada) that market through short food supply chains. We adapted the concept of a complex activity system and the Work Assessment method to reflect the context in Quebec and the unique features of integrated farming models based on direct marketing. In total, we analyzed work organization on 32 Quebec farms to determine how added value and work hours are allocated among different activity segments. Our analysis of apparent labor productivity highlights the heterogeneity of farms involved in direct marketing as every farm studied had a unique profile. In addition, work patterns reflected the background, choices and skillsets of farmers and different combinations of production factors were utilized. Overall, labor productivity was lower in production-related tasks, although this was often offset by higher productivity levels in other activity segments. Our results indicate that greater productivity in the areas of processing or distribution allows farms in short food supply chains to be financially sustainable. The findings of our study also confirm that farmers involved in direct marketing work hard to sustain their activity systems, even though net earnings are often low when compared to the amount of effort involved. However, farmers are partially compensated in other ways, such as through client appreciation and work enjoyment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".