Better Together: Fostering Student-Level Intercultural Competence Through Collaborative Online International Learning (COIL) and a Collaboratively Created Assessment Tool
Bibliographic record
Abstract
Many Japanese universities have been undergoing processes of internationalization to mitigate demographic realities that conspire against robust student enrolments. These internationalization initiatives often attract students from other Confucian-based contexts who are expected to move away from their homeland and fully integrate into an unfamiliar, Japanese environment. Resultantly, international student needs are often camouflaged by cultural similarities that favour group harmony and collectivism over more equitable approaches to learning. Therefore, this Organizational Improvement Plan (OIP) aims to serve student needs by fostering the development of intercultural competencies through a pilot collaborative online international learning (COIL) project open to all students, and the creation and adoption of a context-specific rubric for intercultural competence assessment at a small, private, Japanese university to make the on-campus environment more inclusive for all students. A combined servant leadership and creative leadership approach is a foundational complement to traditional Japanese organizational practices for leading the change effort. Moreover, a context-specific adaptation of Appreciative Inquiry (AI) is supported by the Change Leader’s Roadmap (CLR) in the implementation process. A context-specific, critical-theory-supported approach to AI is also fundamental to the monitoring and evaluation process. The resulting project is focused on increasing student-level intercultural interactions to better align the university’s public-facing policy documents with on-campus practices to make the learning environment more inclusive.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".