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Record W2123839566 · doi:10.1215/18752160-2392710

On Labor and Creative Transformations in the Experimental Fields of the Philippines

2013· article· en· W2123839566 on OpenAlexaff
Chris Kortright

Bibliographic record

VenueEast Asian Science Technology and Society An International Journal · 2013
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsSituatedSociologyProcess (computing)EthnographyProduction (economics)EpistemologyField (mathematics)Work (physics)Social scienceComputer scienceEngineeringEconomicsMathematicsAnthropologyArtificial intelligenceMechanical engineeringPhilosophy

Abstract

fetched live from OpenAlex

Through an ethnography of the C4 Rice Project's sorghum experiment in the Philippines, this article analyzes particular practices in experimental rice fields and how rice researchers understand their work through specific material practices and engagements with the plants. Returning to the critiques of disembodied science, the author looks at the particular, situated, and subjective labor that researchers do in the fields to argue that these relationships offer different and richer ways to understand scientific knowledge production and practices. Drawing out a distinction between working on plants (the human as producer and plant as passive raw material) and working with plants (a process of humans and plants working together in a situated and particular relationship), the article offers an different approach to Marx's concept of labor by incorporating nonhumans as active and relational actors in the labor process. Labor, then, can be seen as a creative relationship between humans and nonhumans situated in particular times and places.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0140.033
Scholarly communication0.0040.004
Open science0.0020.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations27
Published2013
Admission routes1
Has abstractyes

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Same venueEast Asian Science Technology and Society An International JournalSame topicAgriculture, Land Use, Rural DevelopmentFrench-language works237,207