Bibliographic record
Abstract
In attempting to use a realistic evaluation approach to explore the role of Community Parents in early parenting programs in Toronto, a novel technique was developed to analyze the links between contexts (C), mechanisms (M) and outcomes (O) directly from experienced practitioner interviews. Rather than coding the interviews into themes in terms of context, intervention elements (mechanisms) and outcomes separately and which could be assembled into CMO configurations by the analyst, they were coded as linked dyads and triads directly from the practitioner narratives. Out of all of the linked codes entered, there were a maximum of three with the same combination, presenting challenges for typical qualitative data analysis. This article examines a novel technique that was developed in an attempt to expand this method beyond the circumstances described in the realistic evaluation literature to date. The bulk of the article focuses on the linked coding and analysis procedures, the challenges faced, and the original solutions that were developed to analyze the CMO relations and generate the mid-range theories necessary to move to the next stage of a realist evaluation approach. The features that distinguish this linked coding method from other methods (e.g. Qualitative Comparative Analysis), the major benefits and drawbacks, the utility of the approach within evaluation practice, and its application to realist synthesis and research are discussed.
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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 | 0.070 | 0.151 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.003 |
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".