Bibliographic record
Abstract
In this article, I provide an environmental scan of experiential learning (EL) activities – internships, community engaged learning, co-ops, and practicum placements – offered by Canadian communication / media studies programs. These forms of ‘hands-on’ pedagogy can provide students with an opportunity to put their academic training into practice, to gain ‘in the field’ experience, and to collaborate with myriad community partners. However, the growth of EL must be contextualized within the neoliberalization of higher education in Canada, including concerns that universities are being cultivated as utilitarian conduits for job preparedness in a capitalist society. With the demand for EL opportunities unlikely to diminish, I argue that our field needs to proactively engage in determining the future of this form of pedagogy and ensure that it is ethical for all involved in the process. This discussion is informed by anonymous, semi-structured interviews I conducted with faculty, staff, students, and community partners associated with a selection of communication / media studies programs in three Canadian provinces. Further, I highlight recent shifts that have taken place in Ontario vis-à-vis EL as a case study to demonstrate the fast and furious policy changes underway at a provincial level.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.022 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.037 | 0.002 |
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".