Adaptive Case Study-Mixed Methods Design Practices for Researchers Studying Complex Phenomena
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
The methodological purpose of this article is to generate practical guidance for researchers studying complex phenomena through an adaptive case study-mixed methods (CS-MM) design. We describe CS-MM design adjustments made in response to our rapidly changeable research conditions that make complex phenomena challenging to study. We leverage Guetterman & Fetters’ (2018) CS-MM design recommendations while discussing specific adaptive design practice areas and advancing new insights gleaned from an expanded 21-month timeline when compared with a previous 4-month CS-MM study. We draw upon Mike Fetters’ extensive scholarly contributions while striving to continue his legacy for a better world and embodying Mike’s “kind mentoring” approach in our team-based study of the complex phenomena involving the response to a public health emergency.
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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 | 0.432 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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