Advancing Healthy and Socially Just Schools and Communities: An Interdisciplinary Graduate Program
Bibliographic record
Abstract
Advancing Healthy and Socially Just Schools and Communities is a four-course graduate certificate program collaboratively developed by an interdisciplinary team comprised of faculty from the fields of Social Work and Education at a Canadian university. The aim of this program is to facilitate systems-level change through enhancing the knowledge and skills of graduate students from disciplines such as social work, education, and nursing who work with youth in schools and communities. The ultimate goal of this systems-level change is promotion of healthy youth relationships and prevention of violence. The topics for the four courses in the program include the following: promoting healthy relationships and preventing interpersonal violence, recognizing and counteracting oppression and structural violence, addressing trauma and building resilience, and fostering advocacy and community in the context of social justice. The development and pedagogy of the certificate program are described, along with findings from a pilot study designed to examine the utility and feasibility of the initial certificate offering. Experiences with the program to date highlight the potential for improvements in graduate students’ attitudes, beliefs, and confidence regarding what constitutes violence and their role in responding to it.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".