The Reconstruction of Ibn Khaldun’s Grand Design of History-making: The Underlying Metaphysical Cause
Bibliographic record
Abstract
In his attempt to construct the scientific causal narration of history, Ibn Khaldun became a reductionist by concluding that the once ambiguous phenomena of how “history becomes” is the result of two interconnected causes: ijtima’ and ‘asabiyyah. Firstly, this article tries to expose the Grand Design of History-making by connecting Ibn Khaldun’s different dimensions as part of one comprehensive theory. However, there is a logical problem in Ibn Khaldun’s proposal of causal narration of interdependency between causes, which is impossible to solve scientifically and philosophically. Hence, this article attempts to reconstruct the Grand Design of history by eliminating that logical problem. It has been reduced to a proposed final cause that cannot be reduced further. This final cause is an active and dynamic concept of Time, which has been proposed and defended scientifically and philosophically by engaging in different branches of natural, philosophical and cognitive sciences, hence validating the claim and its complete cycle. It might be a new approach to justify History-making with a scientific concept of Time, though it does not rule out the possibility of such arguments. Therefore, it is intended to open a new possibility to view the formation of history and civilisation by the omnipresent phenomenon of time and its role, which is inherently metaphysical and goes against the modern concept of History-making.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".