Community-Based Research and the Faith-Based Campus
Bibliographic record
Abstract
Over recent decades a significant shift has been taking hold on campuses of higher education in Canada and around the world. It is a shift towards community engagement. In this article our focus is on the research aspect of community engagement, and explores how this shift towards community-based research is playing itself out on the faith-based campus. We provide examples of two Canadian faith-based universities (Crandall University and Tyndale University College & Seminary) who were involved in a two-year community-campus research partnership called “The role of churches in immigrant settlement and integration”. Reflecting on this experience we learned that, similar to other institutions of higher education, an intentional shift towards community-based research on the faith-based campus requires attention to both the internal and external drivers that support such a shift. We also learned that faith-based campuses have their own unique ethos and therefore have distinctive drivers that can be leveraged to support such a shift. While our learnings arise out of the experience of two participating universities, their applicability may be of interest to other faith-based campuses in Canada and elsewhere.
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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 | 0.023 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.069 |
| Scholarly communication | 0.016 | 0.007 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".