MétaCan
Menu
Back to cohort
Record W2075339138 · doi:10.1080/15330150390256782

Biodiversity Contests': Indigenously Informed and Transformed Environmental Education

2003· article· en· W2075339138 on OpenAlexaff
Vijaya Sherry Chand, Shaileshkumar Shukla

Bibliographic record

VenueApplied Environmental Education & Communication · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsCONTESTEnvironmental educationRelevance (law)ApprenticeshipPsychologyDiversity (politics)PreferenceTest (biology)Traditional knowledgePedagogySociologyEcologyPolitical scienceGeography

Abstract

fetched live from OpenAlex

The ‘biodiversity contest’ is an educational innovation designed to uncover the plant diversity knowledge of children. This article, based on the experiences of the winners of 31 such contests, seeks to identify the methods through which children learn from their elders and the beliefs that the elders communicate to them. While elders develop in children knowledge about plants, they do not communicate a belief in active conservation. Though elders have a culturally determined preference for boys as apprentices, they do accommodate the education of girls. Systematic instruction, demonstration, questioning to test knowledge and memory, encouraging observation, and supervised practice, are methods the elders use during an extended apprenticeship. The contests have helped recognize the knowledge that children have acquired outside the school, and have helped teachers introduce curricular relevance.

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)0.003
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 score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0070.007
Scholarly communication0.0030.003
Open science0.0010.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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
Published2003
Admission routes1
Has abstractyes

Explore more

Same venueApplied Environmental Education & CommunicationSame topicEnvironmental Education and SustainabilityFrench-language works237,207