Exploring the Use of Trauma Informed Practices in Campus as Lab Programs: Learnings from a Workshop Series
Bibliographic record
Abstract
With the intersectional challenges of the climate crisis, the COVID-19 pandemic, and mental health challenges in various forms, empowerment can hold a significant key to mitigating and preventing traumatic experiences at post-secondary institutions. Campus as Lab (CaL) is a growing trend in higher education whereby students, faculty, and staff use experiential learning and applied research projects to advance sustainability on their campuses. It is a unique, empowering learning methodology that can synergistically benefit academic and operational sustainability efforts at post-secondary institutions. In July 2021, a group of professionals who support or lead CaL initiatives gathered to participate in four Summer Series webinars to explore the use of trauma informed practices in CaL programs. This paper provides a high-level overview of the Summer Series webinar structure and explores how participants identified opportunities to use a trauma informed framework for future CaL initiatives. Because of the Summer Series webinars, we believe there is a need for greater familiarity of trauma informed practices on campuses and amongst sustainability staff. Future research could explore the broader application of trauma informed approaches in the various fields of sustainability within post-secondary institutions.
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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 | 0.027 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".