The Improbable Success of the Petit-Poitou Company, 1650-1720: Agency and Management at the Crossroads of Social and Environmental History
Bibliographic record
Abstract
The Petit-Poitou Company stands out as the first successful large-scale drainage enterprise in France. Created in 1640 by a group of local officials and landowners, by 1646 the Company had completed the operation of draining nearly 4,000 hectares of marshland for farming. This article examines the seventy years following this initial success in order to better understand how the Company overcame significant environmental, social, and political challenges to emerge as a stable and profitable enterprise. This success was highly improbable. Flood disasters, dissension amongst the associates, the actions of tenant farmers, and challenges from regional competitors all threatened to destroy its work. But the Company was able to learn from its early mistakes, build consensus internally, and defend itself from the legal attacks of its rivals through a flexible but firm leadership model and a solid understanding of territorial management and court patronage. This article is based on original research using the unique and previously little-known records of the General Assembly of the Petit-Poitou Company and proposes a more comprehensive view of this period of development for the Company. In other words, it offers a microhistorical analysis that can add to current debate about human agency in the environmental and social history fields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".