Causas de los Procesos Comportamentales: Un diálogo con Mario Bunge
Bibliographic record
Abstract
Mario A. Bunge is one of the most prominent philosophers and humanists of our time. His vast record of publications has covered, among others, epistemology, ontology, ethics, philosophy of natural and social sciences, philosophy of technology, and philosophy of mind. A topic that intersects many of these areas and is recurrent in Bunge’s work is causality. His analyses of the causal principle, and the redefinition of determinism into near-determinism have been applied to different philosophical issues that range from the causal role of neuronal functioning to the laws of social phenomena. Bunge has criticized functionalism, cognitivism, computationalism, behaviourism, and idealism in their attempt to explain human and non-human behaviour. This article results from an extensive interview held with Dr. Bunge in which we discussed a variety of conceptual issues related to the notions of causality and explanation in psychology.
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 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 | 0.014 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.028 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.008 | 0.016 |
| Insufficient payload (model declined to judge) | 0.003 | 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".