Rethinking Postsecondary Access and Engagement for Low-income Adult Learners Through a Community Hub Partnership Approach
Bibliographic record
Abstract
This paper draws upon a case study of a campus-community partnership program in Ontario that delivers tuition-free college courses to low-income adult learners in community hub locations. By co-locating college classrooms in existing neighbourhood gathering places (i.e., a community centre and a public library), our research explores whether integrating college capacity and resources in community hub locations can help increase the accessibility of post-secondary education. In doing so, we address a gap in the research in exploring how community hubs provide a support structure that can help boost the motivation of low-income adult learners and better facilitate their pathway to a post-secondary education. Drawing upon a thematic analysis of interview data, we (a) analyze partners’ perspectives on the community hub–based approach in bolstering the accessibility of higher education, (b) reflect on the process of campus-community engagement underpinning the partnership structure, and (c) critically assess the efficacy of the community hub model in connecting learners with an educational pathway.
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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.001 | 0.003 |
| 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".