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Record W1511634946 · doi:10.31542/j.ecj.233

Understanding Organics at the Grassroots Level: An analysis of Ecuadorian and Canadian perceptions

2014· article· en· W1511634946 on OpenAlexaffvenueabout
Dana Dusterhoft Jason Bradshaw

Bibliographic record

VenueEarth Common Journal · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsMacEwan University
Fundersnot available
KeywordsOrganic farmingGrassrootsCertificationBusinessAgricultureProduction (economics)IndigenousInterviewScale (ratio)Organic productionTraditional knowledgePopularityPolitical scienceGeographyEconomics

Abstract

fetched live from OpenAlex

There is a growing public concern over the genetic alteration and use of chemicals in conventionally produced agriculture. The perceived risk of such agricultural production has prompted the rising popularity of organic alternatives in both developed and developing nations. These products are defined by their reliance on traditional means that do not require the use of harmful chemicals or pesticides in their production. The organic movement in South America has been defined not only by perceived risks, but also by a desire to preserve traditional ways of life. This is accomplished through grants and funding to indigenous farmers in these regions, allowing them to continue their practice. By interviewing a number of individuals in Ecuador and Canada involved in three levels of the organic process (consumers, distributors, and producers) this study determined common cultural and intercultural conceptions of organic practices. These findings were then related to a number of recommendations for three distinct systems (small-scale farming, free trade, and certification) that are currently relevant to the organic movement.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.080
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0090.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.052
GPT teacher head0.221
Teacher spread0.169 · 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

Citations0
Published2014
Admission routes3
Has abstractyes

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