Fighting for Political Freedom: Comparative Studies of the Legal Complex and Political Liberalism by Terence C. Halliday, Lucien Karpik, and Malcolm M. Feeley, eds.
Bibliographic record
Abstract
Most mainstream media coverage of lawyers tends to focus on individual court rulings or spectacular cases of individual character deficiencies within the legal profession. Consequently, society in general has formed a world view that tends to overlook the role of legal actors in struggling for basic rights in all except the most exceptional cases. Fighting For Political Freedom: Comparative Studies of the Legal Complex and Political Liberalism thus fills an essential gap in providing a view into worlds where “ordinary” members of the legal complex regularly play pivotal roles in advancing the causes of liberal society. The book frames its arguments by defining a “legal complex” whose core is composed of lawyers and judges, but extends far beyond this to include all legally trained personnel in a society, including civil servants and prosecutors involved in administering justice. The most impressive contribution made by this book is the universality of the theory of the legal complex’s relationship to political liberalism, and the application of the theory through a collection of case studies spanning four continents. The 16 case studies in the book provide a particularly useful reference guide for academics and human rights activists in the struggle to establish or protect basic human rights in a wide variety of countries.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".