Mixing Research and Religion: Methodological Insights From a Participatory Mixed Methods Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper focuses on a study that exemplifies two types of mixed methods research that have received limited attention in the empirically based mixed methods literature: (a) mixed methods studies that mix research with religion and (b) participatory mixed methods studies in which the line between researchers and the researched is intentionally at least somewhat blurred. The study used a combination of exploratory sequential and participatory mixed methods designs to study one Catholic diocese’s somewhat unique attempt to implement the synod-on-synodality initiative that was being enacted in Catholic churches worldwide. After reviewing related literature, describing the study’s context as well as its design and methods, and presenting selected findings, the paper contributes to the mixed methods literature by highlighting five challenges that arose during the study.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.249 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.011 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it