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Record W2615590953 · doi:10.7213/1980-5934.29.046.eno1

O Realismo Científico de Mario Bunge

2017· article· pt· W2615590953 on OpenAlexaffabout

Bibliographic record

VenueRevista de Filosofia Aurora · 2017
Typearticle
Languagept
FieldArts and Humanities
TopicPhilosophy and History of Science
Canadian institutionsMcGill University
Fundersnot available
KeywordsHumanitiesPhilosophyIntuitionPhilosophy of scienceEpistemology

Abstract

fetched live from OpenAlex

Mario Bunge nasceu em Buenos Aires, em 21 de setembro de 1919. Realizou seus estudos na Universidade Nacional de la Plata, obtendo seu doutorado em ciências físico-matemáticas em 1952. Foi professor de Física Teórica e Filosofia em Buenos Aires, entre 1956 a 1966. Em seguida, tornou-se professor de Lógica e Metafísica na Universidade McGill, em Montreal, onde trabalha desde 1966. Bunge recebeu vinte e quatro doutorados Honoris Causa, sendo membro da American Association for the Advancement of Science (desde 1984) e da Royal Society of Canadá (desde 1992). Em 1982, Bunge foi premiado com o Prêmio Príncipe das Astúrias, em 2009 com a bolsa Guggenheim e, em 2014, com o prêmio Ludwig Von Bertalanffy em Complexity Thinking. É autor de dezenas de livros, entre os quais estão Metascientific Queries (Charles C. Thomas, 1959); Intuition and Science (Prentice-Hall, 1962; Greenwood Press, 1975); The Myth of Simplicity. (Prentice-Hall, 1963). Scientific Research, 2 volumes (Springer, 1967); Foundations of Physics (Springer, 1967); Philosophy of Science: From Problem to Theory, Vol. 1 (Transaction Publishers, 1998); Philosophy of Science: From Explanation to Justification, Vol. 2(Transaction Publishers, 1998); Scientific Realism: Selected Essays by Mario Bunge. Ed. Martin Mahner (Amherst, NY: Prometheus Books, 2001); El problema mente – cerebro (Madrid: Tecnos, 2002); Emergence and Convergence (Toronto: University of Toronto Press, 2003), entre outros.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0060.002
Scholarly communication0.0040.001
Open science0.0020.000
Research integrity0.0000.001
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 head0.066
GPT teacher head0.279
Teacher spread0.214 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations15
Published2017
Admission routes2
Has abstractyes

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