INTERNATIONAL FIELD SCHOOL FOR FIRST-YEAR ENGINEERING STUDENTS
Bibliographic record
Abstract
Engineering graduates increasingly find that they are part of teams that draw a multi-disciplinary membership across a broad range of cultural, socio-economic, and linguistic backgrounds. Although engineering students often have the opportunity to participate in international projects (e.g. co-operative education programs, study abroad), formal international field schools are not typical within engineering curricula, particularly at the first- and second-year level. To provide an early introduction to intercultural perspectives, first-year engineering students at Vancouver Island University (VIU) participated in a field school at Tra Vinh University (TVU) in Tra Vinh Province, Vietnam over a period of three weeks. This field school consisted of a number of cultural and engineering activities, and involved pairing of students at both TVU and VIU for the duration of the experience. To measure student response during the field school, participating VIU students completed the on-line Intercultural Effectiveness Scale questionnaire pre- and post-experience. Students at both institutions also completed reflection exercises throughout the three-week period. This feedback suggested each student pairing continuously developed skills necessary to overcome linguistic, cultural, and technical barriers to learning and growing over their time together. Students described an enhanced understanding of self, and an increased likelihood to further participate in intercultural experiences.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.115 | 0.029 |
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".