Fostering Intercultural Competencies in a Language Instruction for Newcomers to Canada Program
Bibliographic record
Abstract
This research aims to add to the scholarly discourse on the topic of fostering intercultural competencies (IC) in Language Instruction for Newcomers to Canada (LINC) programs. This single qualitative case study explored how IC are perceived and fostered in a LINC program and sought to understand the challenges associated with fostering IC. The study’s data sources were: (a) Semi-structured interviews with program administrators, instructors, and students; (b) In-depth analysis of program documents, and (c) Field notes from classroom observations. Findings from the study indicated that students, instructors, and program administrators perceived IC as attitudes, skills, and knowledge. Social interactions were a significant finding; all three groups of participants perceived social interactions as IC. In addition, classroom and out of class activities offered opportunities to foster dialogue and increase cultural awareness, respect, and curiosity, but fostering IC was not a specific objective in the LINC program. The main challenge that students faced in fostering IC was language, while instructors and program administrators reported time, resources, and knowledge as challenges. Stakeholders in the LINC program, as well as researchers, and practitioners and policymakers of LINC programs may find these findings and recommendations useful in developing a curriculum that embeds intercultural education approaches.
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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 | 0.002 | 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 | 0.018 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".