Modelo de selección de mercados internacionales para la exportación de colágeno hidrolizado
Bibliographic record
Abstract
Objective: This master's thesis aims to identify some multi-variate techniques for the international markets selection (IMS), in addition to applying one of these to recognize the best options for export markets according to real data that give rigor to the process. Methodology: A multi-variate model of IMS is emulated, validated at the theoretical level, which proposes different variables and factors which apply to the tracking of international markets for exports. In this proposal, the leading buyer countries of a given product are pre-selected, in this case, hydrolyzed collagen. Subsequently, factors such as Costs, Trade Barriers, Logistics and Culture are analyzed, including some variables, for each case, and are consulted in official databases through the Internet. This way, this proposal standardizes the information acquired for each variable, generating a number on a scale of 1 to 5 and, finally, defining a total score for each potential market. It will also include a survey aimed at internationalized companies in the city of Manizales to learn about their IMS process in order to contrast it with the proposed technique. Findings: It is possible to say that the most suitable markets for the export of hydrolyzed collagen, according to the criteria taken into account, are the Netherlands; followed by the United States and finally Canada. Practical implications: The technique used in this work can be considered as an instrument for the internationalization of companies that seek to direct their products to other markets; all this, supporting the decision making with reliable information. Contribution: To see how this instrument for IMS gives rigor to the internationalization process based on scientific literature and proven multi-variate theoretical models.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 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".