The Blackwell Guide to Social and Political Philosophy
Bibliographic record
Abstract
Notes on Contributors. Introduction: Social and Political Philosophy - Sorting Out the Issues: Robert L. Simon (Hamilton College). Part I: Core Principles and the Liberal Democratic State: 1. Political Obligation and Authority: A. John Simmons (University of Virginia). 2. Liberty, Coercion, and the Limits of the State: Alan Wertheimer (University of Vermont). 3. Justice: Christopher Heath Wellman (Georgia State University). 4. Equality: Richard J. Arneson (University of California at San Diego). 5. Preferences, Rationality, and Democratic Theory: Ann E. Cudd (University of Kansas). Part II: Liberalism, Its Critics, and Alternative Approaches: 6. Marx's Legacy: Richard W. Miller (Cornell University). 7. Feminism and Political Theory: Virginia Held (City University of New York Graduate School and Hunter College). 8. Liberalism and the Challenge of Communitarianism: James P. Sterba (University of Notre Dame). 9. Liberal Theories and Their Critics: William Nelson (University of Houston). Part III: Pluralism, Diversity, and Deliberation: 10. Deliberative Democracy: James S. Fishkin (University of Texas at Austin). 11. Citizenship and Pluralism: Daniel M. Weinstock (University of Montreal). 12. The New Enlightenment: Critical Reflections on the Political Significance of Race: A. Todd Franklin (Hamilton College). 13. Religion and Liberal Democracy: Christopher J. Eberle (Concordia University-River Forest). Select Bibliography. Index.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.107 | 0.059 |
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".