Intercultural Competency Development Curriculum: A Strategy for Internationalizing Work-Integrated Learning for the 21st Century Global Village
Bibliographic record
Abstract
Abstract Canadian postsecondary institutions are increasing their emphasis on internationalization, sending many students abroad and welcoming students from far and wide onto their campuses. Also, Canadian organizations and multinational corporations have an increasingly diverse workforce. These trends require postsecondary institutions to prepare students adequately for this global village of the 21st century. At the University of Victoria’s (UVic’s) Co-operative Education Program and Career Services, we have created a strategy to help develop global ready graduates using a framework derived from Earley and Ang’s work on cultural intelligence (Earley & Ang, 2003). Cultural intelligence (CQ) is defined as an individual’s capability to function and manage effectively in culturally diverse settings (Ang & Van Dyne, 2008). A recently completed research project to measure the development of cultural intelligence of students participating in the UVic’s CANEU-COOP program formed the impetus for developing this CQ strategy (McRae, Ramji, Lu, & Lesperance, 2016). The strategy involves a framework that includes curriculum for inbound international students, outbound work-integrated learning (WIL) students, and all students preparing to work in diverse workplaces. In addition to developing specific curricula for these audiences, the strategy includes tools to assess the intercultural competencies that students gain during their WIL experiences, as well as helping students use these competencies to transition to the 21st century global village. This strategy and the Intercultural Competency Development Curriculum (ICDC) are discussed in this chapter.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".