Illustrating the Outcomes of Community-Based Research: A Case Study on Working with Faith-Based Institutions
Bibliographic record
Abstract
Incoming immigrants to places like Canada tend to be religious and thereby have sympathies counter to prevailing secularizing trends that emerge in research praxis. This paper presents an illustrative case study of Community-Based Research (CBR) that starts from the community to be studied. We illustrate how CBR can be an effective tool for engaging community stakeholders in solving community problems when stakeholders are part of faith-based institutions. This is accomplished by drawing on Ochocka and Janzen (2014) and Janzen et al. (2016), who discuss the hallmarks of CBR that we used to structure a case study with The Salvation Army (TSA). This paper focuses on TSA as a religious institution and how CBR supports TSA’s adjustment to enhance its relationships with a community it finds itself serving: newcomers. We first outline the hallmarks of CBR and show how they are expressed in our case study. Second, we extend Ochocka and Janzen (2014) and Janzen et al. (2016) by focusing on the functions of CBR to illustrate further the outcomes that can emerge from this sort of approach and make recommendations for researching with faith-based institutions.
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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 | 0.027 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.038 | 0.023 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".