Adapting Criminology Field Placements during a Global Pandemic: Communication, Flexibility, and Contingency Plans in Experiential Learning
Bibliographic record
Abstract
The COVID-19 pandemic impacted the world in ways never imagined. Post-secondary education was no exception. When post-secondary educational institutions switched to online teaching almost overnight, experiential learning programs scrambled to adjust. Research conducted during the pandemic reports on the importance of contingency plans for crises, flexibility, and creativity to ensure students completed their experiential learning terms. Drawing on interviews with 9 students, reflective journals from 20 students, and online surveys completed by 13 community partners, this study explores the impact of the pandemic on field placements that are part of an undergraduate Criminology program at a Canadian university. Findings indicate that the pandemic presented unique challenges to students working in criminal justice system and related agencies and that communication, ongoing support, and flexibility helped mitigate some of the stress these students experienced. Unexpected benefits are also discussed. We offer recommendations for experiential learning in Criminology programs based on these experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".