Building Skills for Success: An ESOL Job Readiness Curriculum for Adult Ukrainian Refugees
Bibliographic record
Abstract
The growing number of Ukrainian refugees in North America has highlighted the need for English language learning resources focusing on employment content. This capstone project aims to fulfill this requirement by developing an ESOL (English for Speakers of Other Languages) curriculum on practical employment skills and communication. The curriculum considers the importance of cultural identity and the impact of trauma on the learner. It is based on Communicative Language Teaching (CLT) principles and Task-Based Learning (TBL). It is intended to assist refugees in making meaningful contributions to their families and communities through work. The curriculum emphasizes communication strategies relevant to North American workplaces, helping bridge the cultural gap between Ukrainian and North American English pragmatics. Curriculum content includes comprehensive lessons on employment, including job searching, resume writing, interviewing techniques, and safety communication. The content is designed to enhance refugee engagement in finding work and complement existing general ESOL curriculums. This research and curriculum aim to empower adult Ukrainian refugees by equipping ESOL instructors with employment-focused materials to help refugees find, secure, and maintain jobs in the United States and Canada.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".