The Art of Evaluation: Reimagining an Employment Program for Migrant Women Using an Arts-Based Approach
Bibliographic record
Abstract
Abstract Arts-based approaches provide a means of reclaiming visual and kinesthetic aspects of inquiry to engage in culturally sensitive research with underrepresented communities (Goopy & Kassan, 2019). In this chapter, the author focuses on using artmaking to stimulate questioning and reflection and to spark dialogue and deliberation in community with others as part of a program evaluation. As a practitioner-researcher, the author inquires into one of the programs that the author managed at a community college—an employment training program for migrant women seeking employment in the hospitality sector. The program sought to provide holistic supports to help migrant women overcome barriers. Grounded in ethnographic principles, this research situated program evaluation within the lived experiences of participants, centering their voices and contexts as integral to understanding program outcomes. The range of research methods the author used—collage-making (Butler-Kisber, 2008), gallery walk (Pink, 2008), and direct observation with field notes—reflects ethnography’s commitment to immersing in participants’ environments and narratives. These methods, associated with qualitative approaches to inquiry (Patton, 2015) and specifically practitioner inquiry, reveal how identity, social capital, and investment intersect for program participants learning English language, literacy, and employment readiness skills. Researching and evaluating a program that the author works in affords both opportunities and limitations. In summary, drawing on ethnographic methods within arts-informed program evaluation and inquiring into the author’s own practice through a social equity lens compels the author as a practitioner-researcher. This chapter illuminates adult education’s transformative possibilities for contributing to, creating, and sustaining a socioeconomically integrated, healthy, diverse, and inclusive society.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.041 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.013 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".