Mixed-Methods Designs in Comparative Public Policy Research: The Dismantling of Pension Policies
Bibliographic record
Abstract
Mixed-methods designs have received burgeoning attention in the academic community during the last decade not only in the social sciences but also in public health research and psychological science (Giddings, 2006; Doyle et al., 2009). Multimethod approaches and techniques of triangulation have a long tradition in these literatures (Campbell and Fiske, 1959; Jick, 1979; Brewer and Hunter, 1989). They have however only recently been translated into a ‘unique research approach that has philosophical foundations, its own terminology, systematic research designs, and specific procedures for designing, implementing and reporting research using this approach’ (Piano Clark et al., 2008: p. 354; see Greene, 2008). Apart from the feature to combine qualitative and quantitative approaches, mixed-methods research differs from multimethod designs by the interwovenness of the underlying research questions that can only be answered through different analyses (Johnson et al., 2007; Morse, 2010). These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.200 | 0.196 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".