Implementación de un plan de negocio para la creación de una empresa de servicios tecnológicos con el fin de satisfacer las necesidades de las Mipymes en la ciudad de Barranquilla
Bibliographic record
Abstract
We live in a globalized and very competitive, so, in the city of Barranquilla there are several companies in all areas and hence the need for a more robust computing infrastructure, reliable and secure, which means that by seeking companies Outsourcing dedicated to technological and computer support. The problem is that within the business sector, there is a group called Small and Medium Enterprises (SMEs) that are disadvantaged because they have to deal with large and powerful companies that have a very advanced systems department, and because they can afford that necessity which has a very high cost, which is the outsourcing service or outsourced service. Micro companies being smaller scales, have a very high technological deficit sustaining because this area is very expensive, since in most of these companies do not have an IT department that can ensure proper computing resources of the company, and that when these resources fail must go to the support of micro computers spend most of which are informal or companies that target the sale of computers and in turn provide support service, thus, does not inspire confidence. Today, Colombia has made commercial alliances with several countries, whose alliances emerged the Free Trade Agreement (NAFTA) and the City of Ottawa is the epicenter and commercial business is more important than the country, therefore, small and medium enterprises (SMEs) should have a well-developed computational field and prepared to cope with the demands of
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".