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Record W3159635671 · doi:10.24908/iqurcp.9099

11. Traditional Knowledge and Modern Agriculture

2016· article· en· W3159635671 on OpenAlexvenueno aff
Colin Robinson, Helen McConnell, Raeya Jakiw

Bibliographic record

VenueInquiry Queen s Undergraduate Research Conference Proceedings · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture, Land Use, Rural Development
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultureContext (archaeology)Sustainable agricultureTraditional knowledgeBusinessFood securityResource (disambiguation)Environmental resource managementGeographyComputer scienceEconomicsEcology

Abstract

fetched live from OpenAlex

As part of our studies in ENSC 315 “Global Food Securities” we are conducting research upon the topic of how traditional ecological and agricultural knowledge and practices can inform and shape sustainable modern agricultural (crop and livestock) strategies in the context of increasing global food demand and decreasing agricultural resource availability. We are using “Traditional Knowledge” as an umbrella term that encompasses agricultural practices spanning from ancient aboriginal knowledge to just before the baby-boomer-incited spike in global food demand. Through rigorous literature review of primary and secondary documents, we are exploring practical traditional knowledge and ecological paradigms and applying them to modern agricultural operation models. We aim to highlight the difficulties that arise in striving for a traditional knowledge-informed sustainable agricultural model, but also to emphasize the benefits this traditional knowledge can provide in the ongoing global food crisis. Through our research, we ultimately hope to explore a workable solution to the over-exploitive agricultural practices of the modern world that is informed by traditional ecological/agricultural knowledge and ecocentric paradigms.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.208
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.118
GPT teacher head0.308
Teacher spread0.191 · 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 designTheoretical or conceptual
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
Published2016
Admission routes1
Has abstractyes

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