Challenging change: transformative education for economically disadvantaged adult learners
Bibliographic record
Abstract
This paper focuses on the results of a mixed-methods study of 13 participants in a Radical Humanities programme designed as a transformative learning space for low-income adults who have experienced barriers to learning. Using demographic questionnaires, semi-structured narrative interviews and course evaluations this study examined participants’ experiences in the programme and the impact on their learning, sense of agency and future ambitions. As the first phase of a longitudinal project on the well-being and agency of under-represented and marginalized learners, this preliminary study revealed five predominant themes emerging from learners’ experiences: (1) self-reflective meaning-making processes; (2) interrelated personal and communal growth; (3) appreciation of diversity; (4) emerging sense of self-as-learner; (5) renewed aspirations. This paper argues that transformative learning for low-income adults is a complex and challenging process that entails participants’ ongoing negotiations of self, learning, and purpose. Understanding and evaluating the effectiveness of transformative learning within a social-emancipatory humanities programme requires attention to the programme’s social context and choices faced by participants. Programme participation fosters increased well-being, deeper relationships and hope for the future. However, these tangible benefits are tempered by constraints of individual and structural systems that, for some students, impose limitations on their ability to enact change in themselves and in their lives.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".