Systemism, Social Laws, and the Limits of Social Theory: Themes Out of Mario Bunge’s
Bibliographic record
Abstract
The four sections of this article are reactions to a few interconnected problems that Mario Bunge addresses in his The Sociology-Philosophy Connection, which can be seen as a continuation and summary of his two recent major volumes Finding Philosophy in Social Science and Social Science under Debate: A Philosophical Perspective. Bunge’s contribution to the philosophy of the social sciences has been sufficiently acclaimed. (See in particular two special issues of this journal dedicated to his social philosophy: “Systems and Mechanisms. A Symposium on Mario Bunge’s Philosophy of Social Science,” Philosophy of the Social Sciences 34, nos. 2 and 3.) The author discusses therefore only those solutions in Bunge’s book that seem most problematic, namely, Bunge’s proposal to expel charlatans from universities; his treatment of social laws; his notions of mechanisms, “mechanismic explanation,” and systemism; and his reading of Popper’s social philosophy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".