Bibliographic record
Abstract
Review by Nevad KahteranThe Teaching and Study of Islam in Western Universities as Routledge publication should be applauded in this cacophony in the post-9/11 world with the rise of interest in Islam and Islamic matters across the globe, necessitating an explanation of the authentic teaching of this religion anew in light of the challenges of the present-day situation not only in New Zealand, Australia and Pacific region, including the Canadian context there as well, but world-wide. Among many other efforts taken in the meantime, something similar was done in the European context as earlier Brill's edition of Muslims in the Enlarged Europe: Religion and Society, ed. By Brigitte Maréchal, Stefano Allievi, Felice Dassetto and Jørgen Nielsen (Brill, Leiden-Boston, 2003) with its speacial stress on After September 11: Islam in General and European Muslims. Also, we could add intersting report on Islam on Campus: teaching Islamic Studies at Higher Education Institutions in the UK (Report of a conference held at the University of Edinburgh, 4 December 2006 in: Journal of Beliefs & Values, Volume 28, Issue 3, 2007, pages 309-329), The Islam in the West Program (currently housed at the Prince Alwaleed Islamic Studies Program), among many other undertakings in this regard as good examples of similar efforts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".