Making Food Count: Expert Knowledge and Global Technologies of Government*
Bibliographic record
Abstract
Un grand nombre d'agences internationales ont mis sur pied des « rationalités » et des programmes mondiaux pour gérer des conduites sociales et économiques. En prenant comme point de mire L'Organisation des Nations Unies pour L'alimentation et L'agriculture, cet article examine la gestion à L'échelle mondiale de 1'agro‐alimentaire dans le cadre de ce que nous appelons lestechnologies mondiales de gouvernance.Nous nous appuyons sur les idées de Nikolas Rose sur L'expertise et le gouvernement ainsi que sur une série d'études sur le calcul statistique et la mondialisation. Nous faisons valoir que les formes professionnelles du savoir de L'expert, comme celles fondees sur des classifications et des calculs scientifiques, ouvrent de nouvelles avenues d'intervention et de transformation sociale. A wide variety of international agencies have initiated global programs and rationalities to manage social and economic conduct. Through a focus on the United Nations and its Food and Agriculture Organization, this paper examines the global management of food and agriculture within what we call theglobal technologies of government.In this analysis, we draw on the insights of Nikolas Rose on expertise and government, and on a range of studies on statistical calculation and globalization. We argue that professional forms of expert knowledge, such as those based on scientific classification and calculation, facilitated new spaces of intervention and new knowledges for social transformation.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.031 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".