<i>Weight of Evidence</i> : Participatory Methods and Bayesian Updating to Contextualize Evidence Synthesis in Stakeholders’ Knowledge
Bibliographic record
Abstract
Mixed methods research is well-suited to grapple with questions of what counts as valid knowledge across different contexts and perspectives. This article introduces Weight of Evidence as a transformative procedure for stakeholders to interpret, expand on and prioritize evidence from evidence syntheses, with a focus on engaging populations historically excluded from planning and decision making. This article presents the procedure's five steps using pilot data on perinatal care of immigrant women in Canada, engaging family physicians and birth companions. Fuzzy cognitive mapping offers an accessible and systematic way to generate priors to update published literature with stakeholder priorities. Weight of Evidence is a transparent procedure to broaden what counts as expertise, contributing to a more comprehensive, context-specific, and actionable understanding.
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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 | 0.440 | 0.565 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.020 | 0.014 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.007 | 0.023 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".