Bibliographic record
Abstract
Those teaching in the area of “Islamic law” (however imprecise that term is) can appreciate the difficulty in conveying information on sources and major principles. People unfamiliar with the Arabic language have enough struggles with the new lingo and could benefit from an overview of the main sources, principles and methodologies in pictorial form. This flowchart is a very basic representation or big picture of “Islamic law.” It is best viewed as a digital document where you can adjust the size. It is only an attempt to give some perspective to the introductory student in this area, but nevertheless a caveat is in order at the outset. Any endeavor that attempts to provide a simple overview of a complex system always runs the risk of oversimplification. Clearly, it is an impossible task to set out detailed discussions of the Islamic system's principles, institutions and their interactions and permutations in such a static fashion. For a more detailed and nuanced explanation of most of what is set out on the flowchart, you may wish to download and read Part 5 of Faisal Kutty, The Myth and Reality of Shari'a Courts in Canada: A Delayed Opportunity for the Indigenization of Islamic Legal Rulings, 7 U. St. Thomas Law Journal 559 (2010), available at http://ssrn.com/abstract = 1749046 . The idea is to demystify Islamic law and allow for people to see the involvment of human agency in the process with the objective of making it easier to reform Islamic rulings to address contemporary realities and challenges.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.024 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| 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".