Breaking down silos: Postsecondary leaders collaborating to advance One Health in education.
Bibliographic record
Abstract
This contribution describes how leaders from the University of Calgary Knowledge Engagement team, the manager of One Health at UCalgary and faculty members from Werklund School of Education came together to form a leadership team and plan a transdisciplinary initiative for future implementation in K-12 schools. The One Health initiative at the University of Calgary is committed to tackling complex problems at the convergence of people, animals, and the environment, and the underlying economic and social factors that determine the opportunities for health across all ecosystems. Systems thinking and inviting different perspectives into the conversation provided a greater understanding of the scope of global challenges and how our individual actions impact others and the environment we all share. The authors used a collaborative and dialogic approach to plan a knowledge engagement session with regional public-school educators to consider how inclusion of the One Health approach into existing curriculum could benefit students. In this chapter, the authors reflected on how matchmaking brought the team together as a boundary-spanning and transdisciplinary team and describe their collective actions and leadership in building collaboration and connections with community partners to lay the foundation for a robust outreach program. The reflections suggest that leaders in higher education can break down silos using a complexity paradigm for their leadership and work together across different departments to combine diverse expertise for community engagement.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.010 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.024 |
| Research integrity | 0.004 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".