Globalization, competitive advantages and the evolution of production systems: rural food processing and localized agri-food systems in Latin-American countries
Bibliographic record
Abstract
This paper reviews the rise of geographic concentrations of small food-processing units in rural areas of Latin America, in order to show that, drawing on the literature on the development of clusters, they may represent a type of local productive system, namely Local Agri-food Systems. Furthermore it assesses whether they might compete efficiently in global food commodity chains. In this regard it analyses the specific assets of these systems, drawing on some specific cases, and stresses the conditions that can enable them to compete on national or even global markets in the supply of processed products. These conditions appear to be a capacity for collective action which, in this particular case, will be enhanced by qualification processes of the products creating common assets for the actors involved. These elements could provide a rationale as regards the categorization of clusters according to their efficiency. Actually, although they won't be able to achieve competitive efficiency in every case, some might, and it remains useful to set up a research programme on their trajectories of development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".