Bibliographic record
Abstract
This session will present successful strategies for instructors and internationally‐educated engineering graduates (IEGs) to manage issues of culture and communication in the engineering classroom with a focus on aspects of the innovative Internationally Educated Engineers Qualifications (IEEQ) Program at the University of Manitoba. The session will also be of interest to those working with international students and mature/adult learners.Several factors influence the successful integration of IEGs into Canadian engineering careers including, but not limited to achieving professional registration and navigating professional, cultural and communication differences. Many IEGs in Manitoba opt to take engineering courses at the University of Manitoba to fulfill the academic requirements for professional registration by enrolling as “special students not seeking degree”, or by completing the IEEQ Program. The classroom itself presents many challenges for IEGs in terms of differences in education systems and academic processes, and like the workplace, the classroom can also be replete with cultural and communication differences, and differences in professional practice. These complex challenges can be time consuming and costly for all parties. Kathleen Clarke will provide a helpful framework of effective practices and lessons learned from the ongoing IEEQ experience in Manitoba.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".