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Entrevista Stuart McCook

2018· article· pt· W2983683098 on OpenAlexaffabout
Stuart McCook, Claiton Márcio da Silva

Bibliographic record

VenueFronteiras Revista Catarinense de História · 2018
Typearticle
Languagept
FieldEnvironmental Science
TopicEnvironmental Sustainability and Education
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsHumanitiesArt

Abstract

fetched live from OpenAlex

O trabalho do historiador canadense Stuart McCook vem se tornando cada vez mais conhecido pelos historiadores ambientais brasileiros. Nascido em Vancouver, na Columbia Britânica, o professor McCook trabalha desde 2003 na University of Guelph, Ontário, Canadá, onde recebe e acompanha um importante número de professores, estudantes e pesquisadores brasileiros e latinoamericanos, auxiliando e orientando em pesquisas de doutorado-sanduíche ou pós-doutorado. Mesmo tendo publicado textos mais voltados à América Espanhola, como o livro “States of Nature”, o trabalho do professor é muito bem recebido pelos pesquisadores brasileiros não apenas voltados aos estudos ambientais, mas como veremos nesta entrevista, também junto à História das Ciências e Tecnologia. No Brasil pela terceira vez, esta entrevista foi realizada na cidade de Guarapuava em novembro de 2015, em ocasião da realização da Escola da Sociedade Latinoamericana e Caribenha de História Ambiental. A entrevista foi realizada e traduzida pelo professor Claiton Marcio da Silva. A tradução e revisão técnica foi feita pela professora Samira Peruchi Moretto.

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)0.003
metaresearch head score (Gemma)0.015
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: none
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0060.003
Scholarly communication0.0080.007
Open science0.0010.004
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0440.018

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 head0.010
GPT teacher head0.242
Teacher spread0.232 · 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
Published2018
Admission routes2
Has abstractyes

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