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Record W3005846330

The study and use of traditional knowledge in agroecological contexts

2019· article· en· W3005846330 on OpenAlexaff
Carolina Alzate, Frédéric Mertens, Myriam Fillion, Aviram Rozin

Bibliographic record

VenueAmericanae (AECID Library) · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Rural Development Research
Canadian institutionsUniversité TÉLUQ
Fundersnot available
KeywordsAgroecologyGeographyAgricultureArchaeology
DOInot available

Abstract

fetched live from OpenAlex

The importance of researching and maintaining traditional knowledge is a concern within contemporary academic debates and public policies. Scientist of different disciplines have recognized this importance, indicating this is a broader interdisciplinary issue. Specifically, within the field of agroecological science, the concept of traditional knowledge is basic to the analysis of agroecosystems. This essay aims to analyze, within scientific papers, the approaches to traditional knowledge through agroecological studies. First, insights from traditional knowledge studies in socio-ecological systems are presented as a wider view. Secondly, papers that illustrate agroecological approach to traditional knowledge and the usage of participative research methodologies are systematically reviewed to the forward development of five propositions: 1) traditional knowledge dynamics, 2) importance of traditional knowledge and professional’s ethics, 3) methodologies used for traditional knowledge gathering, 4) subjects of study in agroecological and traditional knowledge studies and 5) the integration of traditional knowledge with scientific knowledge.

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)0.018
metaresearch head score (Gemma)0.020
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.009
Science and technology studies0.0050.043
Scholarly communication0.0130.014
Open science0.0020.006
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.225
Teacher spread0.199 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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