Boundary-spanning: Engagement across disciplines, communities, and geography
Bibliographic record
Abstract
Narratives from 3 presenters at the closing session of the 2013 Engagement Scholarship Consortium Conference demonstrate that higher education institutions and communities can forge deep and sustainable relationships to address the wicked problems in their countries and communities. University leaders in Nigeria described how students and faculty at the American University participate in service-learning courses and programs that have generated important local economic impacts. A community partner described the impact on educational access and civic leadership for a partnership between a Brazilian high school curriculum provider and a U.S. university, Texas Tech. A young Canadian scholar who works with marginalized, stigmatized, and excluded communities in the world described these partners as environmental heroes and shared a powerful vision of university and community collaboration across the globe. Together, these narratives weave a vision for global partnerships that have tangible impacts for peace, economic security, educational access, and quality of life
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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 | 0.017 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.046 |
| Scholarly communication | 0.014 | 0.016 |
| Open science | 0.002 | 0.036 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".