Reimagining an employment program for migrant women: From holistic classroom practice to arts-informed program evaluation
Bibliographic record
Abstract
This dissertation explores how arts-informed program evaluation contributes to the understanding of an employment program, which was reimagined holistically, for women with immigrant and refugee experience who face barriers entering the Canadian workplace. My practitioner inquiry focuses on a program I managed at an urban community college in partnership with a local community organization. The program supports the development not only of job skills, and English language and literacy, but of social identities that can contribute to success in the search for employment. The decision to launch a women-only program allowed me to surface the experiences and additional burdens conventionally carried by women—for instance, the challenge of childcare as well as periods of absence from the workforce. I used collage-making workshops to learn how these women experienced the program in order to gather knowledge that does not come into focus in the usual standardized evaluation forms or surveys. These arts-informed evaluations enabled students to reflect on the possibilities that the program had afforded them. Informed by theories of social capital and imagined communities and futures, my analysis of their stories showed me that a caring, localized context was paramount for learning. As a practitioner-researcher collaborating with an inquiry community of researchers and drawing on multiple sources of observational, group, and interview data, I was able to explore how, for these migrant women, investment in language and literacy learning in an employment program contributes to the development of confidence, identity, and social relationships, which enables them to overcome barriers. I also argue that a broadened access to an imagined community and imagined future opened up possibilities for the women, which impacted positively on their investment in language learning and their social identity as employable but also as mothers, citizens and community members.
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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 | 0.076 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.005 | 0.023 |
| Research integrity | 0.003 | 0.007 |
| 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".