The Unity of Efficient and Final Causality: The Mind/Body Problem Reconsidered
Bibliographic record
Abstract
In this paper, I argue that it is in the fourteenth century that the problem of the compatibility or unity of efficient and final causality emerges. William Ockham and John Buridan start to flirt with a mechanized view of nature solely explainable by efficient causality, and they hence push final causality into the human mind and use it to explain for example action, morality and the good. Their argumentation introduces the problem of how to give a unified account of the world, that is, how are nature and freedom compatible. In the paper, I set up the discussion by going through some of the problems associated with final causality in the seventeenth century and show that Ockham and Buridan's problems are similar. I then argue using a formulation from Leibniz's Monadology that the problems here traced should be seen as versions of the mind/body problem.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.032 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".