A Critical Reflection of Generalization in Mixed Methods Research
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.
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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 | 0.692 | 0.746 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.015 | 0.086 |
| Scholarly communication | 0.025 | 0.046 |
| Open science | 0.009 | 0.023 |
| Research integrity | 0.020 | 0.061 |
| Insufficient payload (model declined to judge) | 0.003 | 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".