La Implementación de Pasantías Obligatorias en los últimos dos Semestres de las Licenciaturas: Un Modelo para Tijuana basado en Evidencia
Bibliographic record
Abstract
This study examines the lack of implementation of mandatory internships in universities in Tijuana, despite the region’s economic dynamism and the persistent gap between academic training and labor market needs. Using a mixed-methods approach, including surveys of graduates, interviews with employers, and comparative analysis of international case studies, this research assessed the potential impact of integrating structured internships into the final two semesters of undergraduate programs. Statistical results show that graduates with internship experience are up to seven times more likely to be employed in their professional field, report higher starting salaries, and experience a shorter transition to employment compared to those without such experience. The qualitative analysis confirmed that the model’s effectiveness depends on quality mentorship, curricular integration, and fair remuneration. Tensions were identified regarding SMEs’ capacity to finance internships and the need for institutional incentives. The proposed model, inspired by German and Canadian experiences, suggests dedicating the last two semesters to supervised internships with joint evaluation by universities and companies, supported by a tripartite committee (academia-business-government). The findings conclude that this framework can enhance youth employability, reduce unemployment, strengthen local human capital, and position Tijuana as a hub of innovation and regional competitiveness.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".