France: a veil over the past
Bibliographic record
Abstract
The Nazi victories in Europe cast a long shadow over all the countries the Germans occupied. For none is this more true than for France. Hitler had allowed a French government to continue to function, and this Vichy regime under Marshal Henri Philippe Pétain enjoyed the support of the great majority of French people in 1940: for them the war was over. Vichy represented adjustment to the new realities and reconstructions, for the ‘old France’ had demonstrated its rottenness in defeat. There appeared to be no real alternative to ‘honest collaboration’, carrying out the terms the Germans had imposed. But where did honour end? Vichy militia and police helped the Germans to arrest other French citizens to be handed over to Gestapo torturers. Then the Jews were rounded up to be sent to their deaths in the east, not only the foreign refugees admitted before the outbreak of war, but French men, women and children. The war produced great heroes in France: men and women risking their lives for the persecuted, and for the Allied cause. But there were tens of thousands of French men and women who served Vichy France, some in important roles, others in minor capacities, from Pierre Laval, the prime minister to the lowliest policeman or civil servant. They made their living serving the state, and the great majority were able to continue their careers after the war, with no apparent stain on their character.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.007 |
| Scholarly communication | 0.019 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".