Bibliographic record
Abstract
This article argues that western academia has largely eschewed dialogical understanding in favour of Marxist-inspired accounts and poststructuralist theorizing of the Muslim world. The over-abundance of the critique of ideology, other forms of `ideological demystification' and anti-essentialist theorizing have resulted in the failure of the human sciences to adequately understand the emergence of contemporary Islamic and Islamist fervour and its relationship to modernity. In contrast to much, but not all, of the literature on Islam and modernity, the article develops a reconstructed dialogical theory, which draws upon both Gadamerian hermeneutics and interreligious dialogue, as a means to take more seriously the truth-claims of the Islamic Other. By drawing upon notions of `suspicion' and `silence' from interreligious dialogue, a reconstructed dialogical model can overcome the absence of critique, a charge often levelled against hermeneutic dialogue, without resorting to the Enlightenment mode of critique. Although a reconstructed dialogical framework is not without its own problems, for instance how to overcome the gap between a foundationalist Islamic worldview and an increasingly post-foundationalist secular one, it provides a better prospect for understanding the Other.
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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.007 | 0.032 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".