Learning and teaching of Islamic economics: conventional approach or Tawhidi methodology
Bibliographic record
Abstract
Purpose This paper aims to discuss the methodology of mainstream Islamic economics and also gives an alternative approach which is yet not very much taught in the different academic institutions, i.e. Tawhidi methodology. From the curriculum of the different academic institutions and also from the literature, it is observed that mainstream Islamic economics is the imitation of the conventional economics and mainly neoclassical economics. Maqasid-i-Shari’ah is not matching with the Tawhidi one. Design/methodology/approach It is based on the self-observations of the authors where they taught during their academic career. Findings This study found that the mainstream Islamic economics could not be able to solve the local and global issues because it is the replica of the conventional economics only there are some injunctions of Shari’ah. Research limitations/implications This study gives the guideline to the student of Islamic economics that how they will be able to understand the methodology of Islamic economics and finance. Practical implications It provides the guidance to the academicians and policymakers, especially those belonging to the Muslim countries. Social implications It also provides the glimpses to the social scientist about the solutions of the social and economic issues at the local and global levels. Originality/value It is an original effort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".