Bibliographic record
Abstract
This study explored the responsiveness of two Ontario Colleges (called Eastern and Western for the purposes of this study) in the Greater Toronto Area (GTA) to internationally trained immigrant (ITI) students accessing college education for retraining purposes. Many highly educated immigrants are unable to have their credentials recognized because professional regulatory bodies and employers are reluctant to accept their previous education and work experience. Therefore, many ITIs access Ontario college education in the hope that, upon graduation, they will find skill-related employment that will contribute to their settlement in Canada. The purpose of this research was to examine how the two participating colleges address the ITI students’ occupation-specific needs in their current institutional policies and practices. \nQualitative research methods, interviews and document analysis, were utilized to examine the admission and program delivery practices at each of the study colleges. Interviews were conducted with 13 ITI student participants and 14 college personnel to provide an opportunity for them to voice their opinions about their college experiences. To provide direction for interpreting and analyzing the research findings, the single- and double-loop organizational learning framework developed by Argyris and Schon (1974, 1978) was used. The findings suggest that Eastern and Western Colleges have different approaches in valuing and placing importance in responding to ITI students’ retraining needs. The data indicated that ITI students at Eastern College were not perceived by college personnel as a unique group of students having specific retraining needs; rather, they were seen as part of the larger student constituency. On the other hand, Western College recognized the ITI students’ distinctive occupation-specific needs and made commitments towards improving its policies and practices to increase the College’s effectiveness in meeting the ITIs needs. \nAlthough limited to only two Ontario colleges, the study findings have some important implications for theory and practice. The findings have contributed to increased awareness and a better understanding of challenges ITI students face in accessing Ontario college education, and it has offered recommendations for college efforts to respond to ITI students’ educational needs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".