International or Cross-Cultural Placement Experience: A Cross-Case Study of Canadian Initial Teacher Education Programs.
Bibliographic record
Abstract
The world is becoming culturally, ethnically and linguistically diverse. Teaching an increasingly diverse population requires teachers to have the knowledge, skills, and dispositions necessary to facilitate understanding differences in culture and how to go beyond their communities literally and figuratively (Grossman & McDonald, 2008). A key ingredient to prepare culturally and globally literate teachers is to provide international or cross-cultural student teaching experiences (Quezada, 2014). This presentation is drawn from a multi-phase research project funded by SSHRC and designed to study how teacher education programs across Canada are preparing prospective teachers for international and intercultural contexts. This presentation is focused on three primary areas: First, the reasons teacher education programs offer international or cross-cultural practicum placements; second, the knowledge, skills, and dispositions intentionally cultivated in international practica; and, third, the career development preservice students develop when exposed to experiences different from their local context. Data are obtained from the interviews conducted with administrators, faculty members, alumni and students from education programs in each of the 10 Canadian provinces visited. The presentation will conclude with recommendations to Canadian education programs aiming at preparing teachers to work in diverse cultural and linguistic classroom locally, nationally, or internationally.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".