O Realismo Científico de Mario Bunge
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".