A New Realistic Evaluation Analysis Method
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.080 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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