Studying political decision-making as a cognitive process: is it interdisciplinary? A bibliometric analysis
Bibliographic record
Abstract
Introduction At the turn of the 21st century, concerns emerged regarding whether research at the intersection of psychology and political science should be regarded as a multidisciplinary subfield within political science or an independent, interdisciplinary field that contributes to both disciplines. More than twenty years later, how does the literature on political decision-making approach this issue? Should this application of political cognition research be viewed as a multidisciplinary subfield within political science, or as an independent interdisciplinary field contributing to both political science and psychology? This study examines the organizational framework of research and the trends in publications within the literature on political decision-making. Methods Through a bibliometric analysis, this study aims to enhance readers’ understanding of the disciplinary characteristics of research in political decision-making. The analysis examines how publications are distributed across various disciplines and among different researchers contributing to the study of political decision-making, as well as the most frequently used methodologies in this field. Results The findings suggest that research tends to be more multidisciplinary than strictly interdisciplinary. This conclusion is based on three observations: (i) most publications are in political science journals; (ii) much of the research is conducted by political scientists; and (iii) the research mainly uses political science frameworks and observational designs despite political scientists’ familiarity with experimental designs. Departmental affiliation is the key factor in predicting cited literature, with political scientists favoring political science research and psychologists leaning towards psychology research. Discussion The results of this study suggest that while political decision-making research draws on expertise from both disciplines, it remains fundamentally anchored in political science. Recommendations include attending conferences outside the researcher’s primary discipline, provided they are relevant to their research agenda. Researchers should explore the various specialized grants and funding opportunities that aim to promote the development of new research questions and testing new methods, theoretical approaches, and innovative ideas. Faculty should integrate various disciplines into the curriculum to offer valuable and broadly applicable knowledge. By promoting open interdisciplinary dialogue, political scientists and psychology researchers can work together more effectively to tackle the challenges of political decision-making 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 | 0.015 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.118 | 0.209 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".