Strategy: Can a Research Methodology Be Proposed from Islamic Sources of Knowledge?
Bibliographic record
Abstract
The study has attempted to propose a research methodology for the subject of strategy from an Islamic perspective. It employed qualitative research methodologies to explore and analyze the content taken from the texts of the Quran and the Hadith; the Islamic Law contained in these texts; interpretation of this law via Islamic Jurisprudence. It has argued to extend this law into multiple layers of the research methodology ensuring whole research cycle takes place within the Tawhidic Paradigm propounded in the texts of the Quran and the Hadith. In doing so, it adopted the model of the research methodology as developed in the Jeudo-Christian or the Western cultural context and tried to replace the research philosophy(es) and reasoning approaches with the Islamic Law and Islamic Logic, enabling the whole methodology to operate within the framework of revelation and human reason at each and every layer and every aspect. This study is a part of the efforts which are being made to explore alternate perspectives in order to overcome the prevailing issues emerging in the classical management theory and practice, including those related to strategy. While works of scholars from cultural contexts different from the western cultures are surfacing in this area, it seems to be useful to also explore the Islamic sources of knowledge for the very purpose. It is to highlight a crucial point that this study should not be considered a way of negating or rejecting the existing body of knowledge, but it is an attempt to bring something which may complement it or provide a new way of looking into the subject of strategy.
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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 | 0.114 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.006 | 0.036 |
| Scholarly communication | 0.033 | 0.036 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".