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The Growing Power of Agricultural Science

2024· book-chapter· en· W4392939283 on OpenAlexaff
J. L. Anderson

Bibliographic record

VenueOxford University Press eBooks · 2024
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsMount Royal University
Fundersnot available
KeywordsCommodificationAgriculturePower (physics)State (computer science)Production (economics)Agricultural productivityPovertyAgricultural revolutionPolitical scienceCommodityEconomic growthEconomicsEconomyGeographyMarket economyLawComputer science

Abstract

fetched live from OpenAlex

Abstract The story of agricultural science is one of growing power; power to increase production and the enhanced authority of scientists. Eighteenth-century farmers and rural elites were interested in agricultural reforms, paying attention to scientists who articulated theories about chemistry and breeding in an attempt to solve production problems. Those elites established laboratories and experimental farms that were the progenitors of state-sponsored agricultural science. Scientists built educational and research institutions that ultimately provided producers with useful knowledge to boost production. They subsequently fought with other scientists for power and for a place of influence in public life. The scientific solutions they offered often cut both ways, solving old problems even as they created new ones, ranging from environmental degradation to the de-skilling of labor. Furthermore, science was yoked to colonial authority, constructing a landscape of commodity production and the social control required to sustain it. Agricultural science has been commodified and deployed with mixed success in the fight against hunger and poverty, most famously in what became known as the Green Revolution. Scientists’ success in developing solutions to the problems of agricultural production, despite some notable shortcomings, has given them unprecedented power over people and the land.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.989
Threshold uncertainty score0.529

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.014
GPT teacher head0.176
Teacher spread0.162 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2024
Admission routes1
Has abstractyes

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