Re-Imagining Higher Education: Time, Learning, and Risk
Bibliographic record
Abstract
This article recommends institutional changes to higher education related to time, learning, and risk that would better serve the contemporary student population and increase opportunities for life-long and interdisciplinary learning. To begin, the changing demographic of university students will be outlined, along with suggestions about how traditional institutional arrangements are no longer conducive to optimal learning environments. Next, a review of the history of the academic year will be provided, that will show a snapshot of post-secondary academic calendars in Canada. Relatedly, a discussion of the potential drawbacks and benefits to accelerated courses will be deliberated, as well as the role of risk in terms of how this shapes students’ course selection. Finally, an example of a pilot program at McMaster University, a large research-intensive university in Ontario, Canada, which is specifically designed to account for the pitfalls outlined above, will be discussed. Taken together, it will be argued that having full-course offerings on a year-round basis, providing various options for course lengths, and adjusting evaluations to reduce students’ conceptions of ‘risk’ will better adapt institutes of higher education for the twenty-first century.
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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.041 |
| Scholarly communication | 0.022 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".