The social facts of democracy: Science meets politics with Mosca, Pareto, Michels, and Schumpeter
Bibliographic record
Abstract
The rapid expansion of the social sciences in post-war America produced a new approach to research on the theory and practice of democracy. Some of the main themes of this approach were borrowed from early sociological critiques of democracy developed by a group of European social scientists who were later called ‘elite theorists’ or ‘Machiavellians’. This article outlines the set of theoretical motifs found in the works of Gaetano Mosca, Vilfredo Pareto, and Robert Michels that became a foundation for the study of democracy in American post-war social science. Writing in response to the perceived problems of social democracy at the time, Mosca, Pareto, and Michels each identified the goals and ideals of mass popular sovereignty as ill conceived and dangerous based on ‘social facts’ derived from empirical observation. These ‘facts’ appeared in later studies of democracy as naturalized or self-evident foundational propositions. Joseph A. Schumpeter’s famous critique of democracy’s classical ideals is one of the most important examples of a theory built on the ‘facts’ produced by the early critics. This article therefore presents an analysis of the role these conclusions played in Schumpeter’s theory, which characterized democracy as a series of mechanisms designed to mediate and control, rather than give full expression to, popular sovereignty.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.026 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".