A Study of the Neurolinguistic Features of Programming Unhealthy Personality Mindset: A Case Study of the Language of Affection in the Qur’an
Bibliographic record
Abstract
For a start, the present study tackles the language of affection in the Qur’an and its role in managing unhealthy personality. Primarily, it provides a full-scale model for programming unhealthy mindsets. The model uses linguistic and neurological tools in the process of analysis. Affectional language is a case in point that shows how language, emotions, and cognition interact to reflect each other. The study highlights the effective role of affectional language in shaping minds. In the same vein, a typical example for the neurological programming process is the Qur’anic representation of the attitudes of some believers at the time of the Prophet Muħammad (PBUH) after a specific battle. This representation reports the initial reaction of the believers which is emotionally shaped. Also, it helps in making proper reactions which are shaped according to the normal pathways of thinking. The process of shaping cognition is carried out according to specific neurological strategies that include the proper process of sensation, retrieval of the previous experience, neuroplasticity, and acceptance. Also, these strategies include self-compassion and compassion for others. Adding to its motivational role in the promotion of forgiveness, it plays the same motivational role for doing justice. Accordingly, the study concludes that the discourse of affection plays an effective role in shutting the door on any potential sign of the hurt-perceived and the righteous indignation reactions. Furthermore, it combats radicalism and extremism in a way that ends all forms of violence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| 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".