Model of Key Factors in the Sustainable Growth of Small and Medium-Sized Enterprises Belonging to the First Nations
Bibliographic record
Abstract
The search for better living conditions, hand in hand with economic development, is a desire shared by all peoples; the First Nations are no exception. In this sense, entrepreneurship is one of the ways to improve incomes and quality of life, both in industrialized and developing societies, which is considered a potential strategy for economic development. This exploratory research presents a model that explains, through causal relationships, the growth of Small and Medium-sized Enterprises (SMEs) belonging to the First Nations, based on the results coming from the analysis conducted within one of the most important First Nations in the Americas, the Mapuche people, located in South America (mainly Argentina and Chile). The framework was developed from interviews with entrepreneurs and owners of Mapuche SMEs, along with an exhaustive analysis carried out through the use of Partial Least Squares (PLS). The owners were consulted about their attitude towards variables that generate accelerated growth in entrepreneurship in different contexts around the world. Subsequently, a model of inter-relationships was generated that sought to explain which variables are determining factors in the growth of SMEs belonging to the First Nations. Through a process of evaluation and depuration, the model proposed here was arrived at, concluding that Constant Training and Commitment to Growth are the most relevant factors in the growth of these companies. Both of these factors are supported by Long-Term Customer Relationships, Differentiation by Quality, Business Skills, and Business Structure, with a special focus on sustainable 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".