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Record W4233974306 · doi:10.1504/ier.2016.10001107

Challenges of preserving indigenous ecological knowledge through intergenerational transfer to youth

2016· article· en· W4233974306 on OpenAlexaff
Service Opare

Bibliographic record

VenueInterdisciplinary Environmental Review · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicIndigenous Knowledge Systems and Agriculture
Canadian institutionsUniversity Canada West
Fundersnot available
KeywordsIndigenousTraditional knowledgeKnowledge transferAffect (linguistics)Cultural transmission in animalsCultural knowledgeEcologySociologyEnvironmental resource managementGeographyKnowledge managementBiologyComputer science

Abstract

fetched live from OpenAlex

Indigenous communities have acquired significant ecological knowledge which is now gaining global recognition. This makes its preservation for the benefit of society essential. Preservation of indigenous knowledge has been pursued through cultural transmission processes which resulted in intergenerational knowledge transfer to youth. Using data from research conducted in two indigenous communities in Ghana, this paper examines key cultural transmission processes including socio-cultural functions that enabled young persons to interact with and thereby acquire ecological knowledge from elders and other knowledgeable persons. It identifies constraining variables such as limited youth focus on, and inadequate hands-on involvement in, traditional aspects of these functions as critical factors that might negatively affect long-term, sustainable use of acquired knowledge. The paper then examines possibilities of using prominent community members as role models for young persons and other measures for strengthening community interactive processes through which intergenerational knowledge transfer could be sustained.

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)0.006
metaresearch head score (Gemma)0.006
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0030.002
Open science0.0010.002
Research integrity0.0010.001
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 head0.038
GPT teacher head0.268
Teacher spread0.231 · 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
Published2016
Admission routes1
Has abstractyes

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