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Record W3014519490 · doi:10.1177/0263775820912757

Agribiopolitics: The health of plants and humans in the age of monocrops

2020· article· en· W3014519490 on OpenAlexaff
Kregg Hetherington

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsBiopowerThrivingGreen RevolutionPoliticsEnvironmental ethicsSociologyPolitical sciencePolitical economyAgricultureGeographySocial scienceLawArchaeology

Abstract

fetched live from OpenAlex

The well-known story of biopolitics tells us that as Europe urbanized, security was increasingly linked to human well-being. What the story tends to leave out is the way that biopolitics also depended on the expansion of monocrop agriculture: the thriving of human populations was enabled by the thriving of non-human food crops, especially grains. As a result, new human diseases were also shadowed by new plant diseases, and a whole other, parallel governmental apparatus built to manage the crop health in rural Europe. During the great postwar development initiative known as the Green Revolution, plant health techniques would be expanded to the Global South in a massive realignment of biopolitical relations. Though the core tradition of biopolitical thought rarely made it explicit, biopolitics was always, in other words, agribiopolitics, a political technique that made certain populations of humans thrive alongside companion crops. Using Paraguay as a site of genealogical engagement, this paper explores agribiopolitical relations through three phases of the Green Revolution, culminating in the current age of monocrops.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.119
Threshold uncertainty score0.109

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.027
GPT teacher head0.211
Teacher spread0.184 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2020
Admission routes1
Has abstractyes

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