Forests and Peasant Politics in Modern France . By Tamara L. Whited. New Haven, CT and London: Yale University Press, 2000. Pp. xii, 274.
Bibliographic record
Abstract
Tamara Whited's carefully researched study of the conflicts over reforestation and changing land use in the alpine départments of Savoie and the Ariège represents a welcome addition to a growing, but still relatively undeveloped, literature on the history of the environment in modern France. While much of the work focusing on forests has tended to trace the evolution of state management practices, Whited examines local, grassroots responses to government policy, and the political, social, and ecological assumptions that were imbedded in struggles between the state and the French peasantry. Whited pays subtle attention to the ambiguities and paradoxes inherent in responses to environmental change at both the national and local levels. She shows, for example, that antagonisms between peasants and state officials were not always immutable. Peasants sometimes adopted the state's language to press their claims, and forest officials sometimes repudiated aspects of state policy. The breadth of this study—which spans a period from the seventeenth to the mid-twentieth centuries—reveals, in a larger sense, the changing and often conflicting ways in which the natural world was used, managed, and imagined in a specific historical context.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".