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Record W4391435830 · doi:10.14295/ambeduc.v28i2.15746

Percepção Ambiental

2023· article· pt· W4391435830 on OpenAlexaff
Michelle Luise da Silva Soares Silveira, Gleice Azambuja Elali, Douglas D. Karrow

Bibliographic record

VenueAmbiente & Educação · 2023
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsBrock University
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

Environmental Perception: A study with IFRN-SGA students Os evidentes impactos das atividades humanas no ambiente têm suscitado debates acalorados sobre a importância da sustentabilidade e da preservação do meio ambiente, fomentando uma desejada conscientização ecológica. Apesar disso, muitas pessoas ainda não compreendem completamente os efeitos de suas ações sobre o ecossistema. Nesse cenário, é importante compreender a perspectiva dos jovens, pois eles serão responsáveis por moldar o futuro do planeta. Este artigo visa analisar os resultados de uma investigação sobre a percepção das questões ambientais por estudantes do ensino médio. Baseado na Psicologia Ambiental, o estudo adotou abordagem metodológica exploratória e quali-quantitativa, e aconteceu no Instituto Federal de Educação, Ciência e Tecnologia do Rio Grande do Norte - Campus São Gonçalo do Amarante. A coleta de dados envolveu aplicação de questionários a 187 estudantes na faixa de 14 a 18 anos matriculados nos cursos técnicos de nível médio e a realização de rodas de conversa com 76 deles. Os resultados indicam que, os participantes compreendem as questões ambientais, porém não se apropriaram efetivamente desse conhecimento, de modo que ele pouco se traduz em comportamentos/atividades e relações afetivas consistentes, condizentes com os princípios de conscientização socioambiental, o que aponta a necessidade da educação ambiental estar mais atenta à percepção ambiental.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0000.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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 designObservational
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

Citations3
Published2023
Admission routes1
Has abstractyes

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