Experiential learning and the university’s host community: rapid growth, contested mission and policy challenge
Bibliographic record
Abstract
This paper examines the recent growth of experiential learning (EL) and the university-community (or so-called town-gown, TG) connections created as a result of this expansion. The research is framed by critical scholarship on the nature and role of the university and the place of liberal education specifically, as well as policy drivers aimed at social and economic impacts from EL. Two subthemes are also examined: first, the role of the arts, humanities and social sciences disciplines in EL expansion and, second, the extent to which TG connections focus on the university's local host community as opposed to more distant and even international arrangements. Mixed research methods including public document analysis and key informant interviews are used to document and interpret EL developments across nine varied universities in Ontario, Canada. The results underline broad sector commitment to EL that in turn creates new and different TG connections for the university. Rapid expansion has brought a variety of challenges identified both by universities and community EL partners. The paper concludes with discussion of policy implications and consideration of the future of EL in light of the 'digital pivot' of the COVID-19 pandemic.
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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 | 0.025 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.022 | 0.021 |
| Open science | 0.002 | 0.021 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.017 | 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".