Bibliographic record
Abstract
This is the second book on parenting by the husband-and-wife team of Dr.Ekram and Mohamed R. Beshir. Muslim Teens addresses an issue of deepconcern to all Muslim parents: how to keep one’s children on the “straightpath” when they reach the turbulent years of adolescence. The authors areprofessionals who have raised four daughters in Canada. Their daughters,now adults, have contributed to the book with insights from a teen’s pointof view, based upon their own experiences of growing up Muslim in NorthAmerica.The first two chapters draw a picture of teens and their environment.Drawing on Ekram’s background in medicine and child psychology, thesechapters describe the turbulent nature of puberty and the physical and emotionalgrowing pains experienced by teenagers. The second chapter alsogives an overview of North American teen culture, which should berequired reading for all immigrant parents raising children in a culture thatcan be viewed as largely antithetical to Islam.Chapters 3 to 6 provide a “road map” for raising teens and presents anoverview of the aims and stages of Islamic tarbiyah (education, upbringing).They advise parents to start early and state that both parents shouldshare a common vision of childrearing and set clear goals, such as spiritualconviction (not just knowledge of Islam) and enabling teens to developstrong and confident personalities. These chapters contain extensivequotations from the Qur’an and Sunnah to support what the authors say,thus enabling readers to gain a solid overview of parenting from an Islamicperspective. The authors also take some basic Islamic principles of social ...
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.102 | 0.027 |
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".