Positioning Theory in Islamic Sermons::Online Messages to Parents.
Bibliographic record
Abstract
Positioning theory offers a theoretical and analytical framework to explore how individuals position themselves or are positioned by others through discourse. Positioning theory provides ways to interpret how the positioning is achieved through the mutual effects of storylines, speech acts, and positions (Van Langenhove & Harré 2003). We examine how male and female preachers position themselves when they advise parents about Islamic values in raising children. The sermon data is from a corpus of twenty online Islamic sermons on YouTube that engage with the theme of family. The sermons were delivered in different settings, such as in Friday services in the mosque or Islamic conferences in auditoriums in various countries, namely the USA, UK, Canada, Sri Lanka, and Qatar. The findings show that the preachers put themselves in a position of authority primarily through their expertise in quoting and interpreting authoritative sacred texts. Preachers' positioning is fluid; they position themselves as a person who delivers God's words, as storytellers, or take a more authoritative position by employing direct commands. It is common in Islamic communities for mothers to have responsibility to teach and raise children. In sermons, the preachers tell stories of paragons of Islamic parenting such as Luqman, male Biblical prophets, and stories of Muhammad to inspire fathers to play their role in helping mothers raise children. First Page
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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 | 0.003 | 0.010 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".