Bibliographic record
Abstract
In this article I focus on Ibn Sīnā's explanation of miracles and argue that he accepts the occurrence of miracles as extraordinary events, despite his theory of the necessity of everything. His explanation of miracles may be considered naturalistic, since it does not require an exceptional divine intervention. First, I analyze Ibn Sīnā's theory of miracles, then I relate it to his explanation of the functioning of the universe. I try to answer how it might be possible to accept both the necessary causal nexus within the world and the occurrence of miracles as violations of the natural laws. Finally, I take into account al-Ghazālī's critique of “philosophers,” including Ibn Sīnā, on their acceptance of a necessary connection between causes and effects, a critique supposedly aimed at saving the possibility and actual occurrence of miracles. I try to make sense of al-Ghazālī's text, vis-à-vis the fact that Ibn Sīnā did not deny the occurrence of miracles, and the fact that his theory provides a possible explanation thereof. Hence, I argue that the real issue between al-Ghazālī and the philosophers he attacked regarding the purportedly necessary connection between causes and effects is not the possibility of miracles, but the proper conception of God.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".