PYMES en América Latina: clasificación, productividad laboral, retos y perspectivas.
Bibliographic record
Abstract
This article presents a general diagnosis of SMEs in Latin America. It is divided into four sections. The first presents a succinct discussion of the classification of firms in the main Latin American countries, comparing them with the stratification of the United States, Canada and the European Union. The second section analyses the importance of the micro, small and medium-sized enterprises (MSMEs) segment and the sub-segment of small and medium-sized enterprises (SMEs) in the economy, through their share in the number of enterprises, contribution to employment, generation of value added and value of exports. The third section reflects on differences in labour productivity and wages, showing the internal and external gaps in the returns to labour that prevail in micro, small and medium-sized enterprises in Latin America. Finally, the fourth section summarises some of the challenges that companies face in order to overcome the main problems that inhibit their growth and access to the international market.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".