Classifying Cases in Federal Studies. An Illustration of why Political Scientists should do more Cluster Analysis
Bibliographic record
Abstract
Typologies are widely used in research on federalism, e.g. to distinguish dual from cooperative or coming-together from holding-together federations. More general, ideal types, archetypes and categories are frequently used in political science research to define concepts and classify cases. As recently as in 2014, Filho et al. pointed out that Cluster Analysis is still hardly used when it comes to developing typologies in political science. Rather, political scientists rely on more intuitive methods or factor analysis. Our paper argues that Cluster Analysis is of great usefulness because it a) focuses on the relationship between cases and not variables and b) draws on empirical data when identifying the clusters. This paper proposes to apply this fruitful approach to the field of federalism to exemplify its major heuristic potential. Furthermore, we emphasize that testing the secondary validity is a crucial step. Our paper provides two original examples from comparative federal politics and public management that illustrate the strength of Cluster Analysis both in testing and generating hypotheses through the establishment of typologies. For both examples, the validity of the Cluster Analysis is tested by checking for correlations between the clusters and the distribution of power. Hence, the typologies established through Cluster Analysis not only define our respective dependent variables related to aspects of intergovernmental coordination within federations and the normative density of evaluation clauses in the Swiss federation, but also offer strong insights in issues of regional autonomy.
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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 | 0.061 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.021 | 0.026 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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; 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".