Exiting Lepušićeva? Croatian Comparative Politics and Political Science a Quarter of a Century from the Beginning of Political Transition
Bibliographic record
Abstract
The author deals with the state of comparative politics in Croatia, and the state of political science more generally, a quarter of a century after the beginning of political transformation. Selective bias in comparative research and underdevelopment of the discipline are diagnosed as the main causes of its unimpressive status in the International community of political scientists. The first part of the article discusses in more general terms the problem of selective bias as one of the most widespread, but also most dangerous mistakes in comparative research. Natural bias is reflected in the choice of only known and available cases, while unnatural bias involves choice only of the cases that confirm the starting hypotheses and exclude those that question or repudiate the hypotheses. In the second part, the author illustrates the selective bias in research of political transformation and regional comparative politics using Croatia as an example. The main cause of natural bias has to do with the fact that many comparativists are unfamiliar with the language, history and politics of the country. This is largely due to large-nation bias and Reliance on selective historical data. Unnatural bias reflects methodological problems in designing research in comparative politics, most often in emphasizing one set of variables at the expense of another, which affects the results of research. In the concluding part, the article deals with the causes of underdevelopment of comparative politics in Croatia.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| 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; 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".