World’s Greatest Challenges: Building Interdisciplinary Understanding and Collaboration among Business and Social Work Students
Bibliographic record
Abstract
Socially engaged students cross all disciplines. Today’s complex and rapidly changing environment poses new challenges for practitioners, students, and educators working within and alongside post-secondary institutions. This research explores how business and social work students interpret and perceive the world’s greatest challenges. Eighty-four students participated in the mixed methods study wherein a workshop was delivered to two social innovation and two social work classes. The workshops provided a forum for students to explore their perspectives on the world’s greatest challenges individually and as part of a group. Differences and similarities are reported regarding student perspectives of the world in which they live. The results reinforce a change in student mindset requiring significant shifts in the development and delivery of social impact curriculum and pedagogy related to creating shared value, social innovation, and changemaking.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.023 | 0.015 |
| Scholarly communication | 0.022 | 0.013 |
| Open science | 0.003 | 0.030 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".