Beyond the Empirical-Normative Divide: The Democratic Theory of Jane Mansbridge
Bibliographic record
Abstract
Jane Mansbridge's intellectual career is marked by field-shifting contributions to democratic theory, feminist scholarship, political science methodology, and the empirical study of social movements and direct democracy. Her work has fundamentally challenged existing paradigms in both normative political theory and empirical political science and launched new lines of scholarly inquiry on the most basic questions of democratic equality, deliberation, collective action, and political representation. Her three best-known books—Beyond Adversary Democracy (1980), Why We Lost the ERA (1986) and Beyond Self-Interest (1990a)—have become part of the political science canon and remain staples on graduate course syllabi decades after their publication. The importance of Mansbridge's work has been recognized by her colleagues through a trifecta of major APSA awards: the Gladys M. Kammerer Award (1987), the Victoria Schuck Award (1988), and, most recently, the James Madison Award and Lecture (2011).
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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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 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".