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Representações da Mata Atlântica e de sua biodiversidade por meio dos desenhos infantis

2007· article· pt· W2016132357 on OpenAlexaff
Maria Luiza Schwarz, Lúcia Sevegnani, Pierre André

Bibliographic record

VenueCiência & Educação (Bauru) · 2007
Typearticle
Languagept
FieldPsychology
TopicAnimal and Plant Science Education
Canadian institutionsUniversité de Montréal
FundersMinistério do Meio AmbienteCritical Ecosystem Partnership Fund
KeywordsHumanitiesPhilosophy

Abstract

fetched live from OpenAlex

Desenhos infantis são instrumentos úteis e significativos que podem ser empregados para avaliar conhecimentos, competências, observações e conceitos de Ciência, além de possibilitar analisar a capacidade de raciocínio. Este trabalho apresenta a análise de 395 desenhos de crianças com idade entre 6 e 14 anos, habitantes de área urbana de Joinville, SC, Brasil, com o objetivo de verificar quais são os conhecimentos desse grupo social sobre a Mata Atlântica e sua biodiversidade. Foram investigados quais os ecossistemas mais representados, assim como a diversidade da flora e da fauna enfatizada. Durante a análise dos desenhos, verificamos que foram abordados quatro temas distintos: "o bom estado de conservação da Mata Atlântica", "o péssimo estado de conservação da Mata Atlântica", "comparações entre o bom e o péssimo estado de conservação da Mata Atlântica", e "recomendações para a conservação da Mata Atlântica". Os resultados mostram que 60,2% representaram "o bom estado de conservação da Mata Atlântica".

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)0.001
metaresearch head score (Gemma)0.002
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.357
Teacher spread0.303 · 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

Citations25
Published2007
Admission routes1
Has abstractyes

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