Reading Europe Against the Grain: A Genealogy of Agricultural Governance
Bibliographic record
Abstract
Utilizing insights from historical and political sociology, this dissertation explores the multitude of ways food, agriculture and rural economy have been governed in Western Europe.As a genealogy of agricultural governance, the project operates on two distinct analytical levels.The first level is analogous to what Charles Tilly labels 'macro' political history.The point here is to interrogate the ways agro-food power has been imbricated with historical processes such as statemaking, geopolitics, postwar reconstruction, welfarism and the neoliberalization of economic life.Second, the dissertation aims to make intelligible how agriculture has been created and recreated as a space of government.This will be done by deconstructing its various problems, strategies, objects and mechanisms over time.The project covers a wide swath of history: from the ancien régime to the EU.Nonetheless, the bulk of the empirical investigation will be centered on configurations of 'European' agricultural governance over the postwar era.The dissertation ultimately draws two important conclusions from its examination of agro-food power.First, the production and supply of food has always been a central space through which populations have been managed by centralized political authorities -a fact equally true at the national and European levels.Second, the strategies and practices of agricultural governance have been formative in building postwar 'European government'.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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 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".