Permissible and Prohibited Businesses in Islam
Bibliographic record
Abstract
Business is a very important thing in the economy, Islam sees business as having to be done but must comply with the rules that have been regulated in islam. This article aims to provide information to readers both student who are prospective entrepreneurs, prospective entrepreneurs/ wati so they can understand in theory how they should and should be a businessman in accordance with Islamic teachings. And it is also hoped that this writing will become an enlinghtenment for traders and entrepreneurs. The research method used in this article is descriptive qualitative. The descriptive qualitative technique used is library research, which is data collection by conducting study studies of books, literature, notes, and reports that have to do with the problem being solved. This research also examines written sources such as scientific journals, reference books, literature, etc. related to research studies. And the results of this study are that there are some values that must be applied by business people in doing business including honesty, fairness, oneness, honesty in business, honesty in production, honesty in selling, honesty in gaining profit. While the value in justice is fairness in business, justice in production, fairness in selling, justice in gaining profit. And the next value is the value of oneness, namely oneness in production, oneness in selling and oneness in gaining profit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".