Estrategias de internacionalización para la exportación del calzado de la empresa Calzados Paredes S.A.C. al país de Chile para el periodo 2014 - 2019
Bibliographic record
Abstract
ABSTRACT \nThe main objective of this thesis is to propose internationalization strategies in order to \nexport the footwear of the company from Trujillo “Calzados Paredes SAC” and \nencourage it’s international openness, through a pre-selection of markets among them are \nEcuador, United States, Canada, Chile and the Dominican Republic, which are still the \nmain destinations of Peruvian exports of footwear in the last 5 years, resulting as the best \nattractive market to export to our neighboring country Chile; this country is interesting for \nhaving a stable economy, economic agreements, is simpler and cheaper to start a \nbusiness with this country; also has a transparent environment, well regulated and \npolitically steady. \n \nIn order to determine the best strategies for internationalization to be used, we take \nadvantage of the national recognition and the entrepreneurship from “La Libertad”, \nespecially the city of Trujillo, having as Information Base the export enterprises from “la \nLibertad”. \n \nThe proposed strategies are differentiation and market penetration, through them the \nenterprise Calzados Paredes S.A.C. would have a better commercial vision and will allow \nthe opening to the Chile’s Market, developing a product of quality to an exigent market, \nidentifying the needs from the segment to which the footwear is intended.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".