Fear and Fitra: Cognitive Science of Religion and Ghazali's Ultimate Agent
Bibliographic record
Abstract
The field of Cognitive Science of Religion (CSR) is a growing field that has made interesting inroads in analyzing various religious traditions. There has been reticence within Islamic Studies in engaging with CSR, for reasons of reductionism. However, there is a fruitful discussion to be had between these two disciplines. I extend Aria Nakissa’s work analyzing Al-Ghazali and make my own assertions regarding the Islamic notion of fitra. I assert that fitra, the inherent disposition for belief in God can be compared with CSR’s claims about the innateness of belief in God. I argue fitra is an epistemic notion whose subcomponent, the wahm (estimative faculty) produces judgments akin to those judgments made by cognitive modules like the hyperactive agency detection device (HADD). This module is subject to sensitivity regarding the detection of agents. I claim that supernatural agents can be inferred from a more local agent when the wahm goes beyond its domain of sensory perceptions, informed by Ghazali’s thought. Lastly, I argue that fear can elicit belief in God and Ghazali demonstrates this observation when he advises fear as a therapeutic device to remediate doubt or apostasy.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".