Bibliographic record
Abstract
1. Introduction: History and revolution: Mike Haynes (University of Wolverhampton), and Jim Wolfreys (King's College London) 2. Radicalism and revisionism in the English Revolution: Geoff Kennedy (York University, Ontario) 3. Twilight revolution: Francois Furet and the manufacturing of consensus: Jim Wolfreys (King's College London) 4. The French Revolution: revolution of rights of man and citizen: Florence Gauthier (Universite Paris VII) 5. Grey masses and Jacobins in 1917: Mike Haynes (University of Wolverhampton) 6. Lenin's mistake: the Bolsheviks and the politics of the civil war: Lars Lih (independent scholar, Montreal). 7. Nazism and Communism. Re-readings of the twentieth century by Ernst Nolte, Francois Furet and Stephane Courtois: Enzo Traverso (Universite d'Amiens). 8. Communism, Nazism, colonialism, assessing the analogy: Marc Ferro (Ecole des Hautes Etudes en Sciences Sociales). 9. What produces democracy? Geoff Eley (University of Michigan) 10. Revolutions: great and still and silent: Daniel Bensaid (Universite Paris VIII).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".