A language exchange program: sustainability innovation in language and culture engagement
Bibliographic record
Abstract
Spanish Educational Laws have been promoting the \nwidespread use of English; as a result, Spanish Uni \nversities are looking for ways to give students more international training in order to prepare them for a future that will increasingly involve global problems and partnerships. Therefore, the Polytechnic University of Madrid, Spain (UPM), and the University of British Columbia, Okanagan, Canada (UBCO) have come together to offer opportunities for international collaboration and learning, thus facilitating virtual encounters among Spanish and Canadian students. The Language Exchange Program between the UPM and UBCO acts as a model for sustainability innovation in language and culture engagement as the students can interact with native \nspeakers in communication tasks. This interdisciplinary initiative supports the latest methodological \nprinciples observed in the Common European Framework for \nLanguages, such as autonomous and life-long learning, self-assessment and peer-assessment as well as the incorporation of new technologies to the learning process. \nAdditionally the ‘virtual’ mobility is provided at no extra cost. This article presents the preliminary results of two virtual exchange programs that have been offering varied forms of study which are venue-independent, and have clearly expanded the range of scenarios for the students on both sides by promoting collaborative work and cultural exchange.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".