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Anticorpos de Gaia no encontro das águas": trajetórias de aprendizagem de jovens nas trilhas do ambientalismo

2010· dissertation· pt· W1504640439 on OpenAlexaboutno aff
Paulo Marco de Campos Gonçalves

Bibliographic record

Venuenot available
Typedissertation
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsnot available
FundersMinistério do Meio AmbienteMinistério da EducaçãoInstituto Brasileiro do Meio Ambiente e dos Recursos Naturais RenováveisMinisterio de Economía y Competitividad
KeywordsSocializationSociologyConstitutionAction (physics)Subject (documents)Perspective (graphical)Identity (music)Social psychologyPsychologyHumanitiesSocial sciencePolitical scienceArtAesthetics

Abstract

fetched live from OpenAlex

AgradecimentosEsta Tese é fruto do apoio, compreensão e afeto de toda a minha família, de professores, de amigos e colegas de trabalho e militância na educação ambiental.O convívio com cada um deles é o cerne da aprendizagem e da relação que tenho com o mundo.Em primeiro lugar, agradeço a meu orientador, Prof. Pedro Roberto Jacobi, verdadeiro co-autor desta Tese, sempre atento ao rigor científico necessário ao trabalho acadêmico e, igualmente, incentivador da originalidade e liberdade intelectual do autor.Nestes dez anos de convivência, desde meu ingresso no mestrado, fui agraciado por sua amizade sincera e múltiplas oportunidades de aprendizagem.É para mim um exemplo de maestro da prática interdisciplinar, pela capacidade que tem de articular sua produção acadêmica com a mobilização de atores de diferentes áreas na construção de ações no campo teórico e prático da educação e da gestão 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)0.007
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 score0.028
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0070.008
Scholarly communication0.0040.002
Open science0.0010.005
Research integrity0.0010.002
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 head0.006
GPT teacher head0.269
Teacher spread0.263 · 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
Published2010
Admission routes1
Has abstractyes

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