A Critical Reflection of Generalization in Mixed Methods Research
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
Mixed methods research, that is, research that integrates qualitative and quantitative methods, has become increasingly popular in program evaluation because of its potential for understanding complex interventions. Despite recent constructive and fruitful developments that have led to the consolidation of mixed methods as a distinctive methodology, fundamental methodological issues such as generalization have received little attention. The purpose of this paper is to provide a critical reflection on how the concept of generalization has been used in mixed methods research. The paper is structured into four main parts. First, we discuss the relevance of external validity and mixed methods research in impact evaluation. Second, we summarize how generalization is conceptualized in mixed methods research. Third, we present the results of a literature review on generalization practices in mixed methods research. Finally, we conclude with a discussion of threats to and strategies for enhancing generalization in mixed methods research.
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 | 0.124 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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