Language learning through cultural exchanges on site
Bibliographic record
Abstract
The Spanish educational system has been engaged in a far-fetching language program that promotes a bilingual training for a high number of students in most primary and secondary state schools. At tertiary level, most universities in Spain, among them the Universidad Politécnica de Madrid-Spain (UPM, require a B2 certificate (CEFR) to enroll in the compulsory subject “English for Professional and Academic Communication” . Even though students are supposed to have achieved a B2 level on completing their secondary education, experience over the last years shows that they are far from this high-intermediate level. Fully aware of this problem the UPM is promoting several programs of Innovation in Education to help students bridge the gap and obtain the B2 certificate. The Language Learning through Cultural Exchanges on Site Program presented in this article is based on a partnership between the UPM and the University of British Columbia, Canada (BCU) . This program offers opportunities for language learning (English-Spanish)through in situ encounters and acts as a model for innovation in language and culture engagement. This initiative aims to follow novel methodological trends such as the promotion of autonomous learning, self-assessment and peer-assessment
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".