Recomendaciones de Liderazgo para los Dueños de Pymes Familiares Exportadoras y no Exportadoras en Nuevo León
Bibliographic record
Abstract
Key Word: Family business; leaders, owners or managers, profits, SME’s business; SME’s exportingAbstract: The main purpose of this document is to state the importance that small and medium-sized enterprises (SMEs) exporting and non-exporting have, as well as family businesses in Mexico´s economy and specifically in the state of Nuevo Leon. It establishes certain family SMEs, exporting and non-exporting, characteristics to take into consideration by Nuevo Leon leaders that seek success under the determinant of profits. It also locates qualities, from our stand point, classic and contemporaries, recommended for managers and administrators of this type of companies.Palabras Clave: Dueños o administradores, ganancias, líderes, negocios familiares; Pymes, Pymes exportadorasResumen: El presente documento tiene como finalidad plasmar la importancia que tienen las pequeñas y medianas empresas (Pymes) familiares, exportadoras y no exportadoras en la economía de México y específicamente en el estado de Nuevo León. Se establecen ciertas características a considerar de las Pymes familiares exportadoras por los líderes nuevoleoneses que buscan el éxito bajo la determinante del aumento de las ganancias. Igualmente se ubican cualidades, a nuestro juicio, clásicas y contemporáneas, recomendadas para directivos y administradores de este tipo de compañías.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".