LEARNING NOT TO SPEAK IN TONGUES: THOUGHTS ON THE LIBRARIAN OF BASRA
Bibliographic record
Abstract
We explore the nature of knowledge and education and how Islamic traditions have shaped understanding of these matters. We contrast this with contemporary images of “Taliban‐like” schools full of rote repetition and harsh, authoritarian literalism. Some of the history of Islamic scholarship venerates a much more generous relationship to knowing. We link these explorations to a recently published children’s picture book, a true story about a librarian in Basra, Iraq, during the recent American invasion. Even if it is a “true story,” we consider what its truth is and whether educators, might or should or could stand by this truth. Key words: Islamic philosophy, curriculum knowledge, knowledge formation, politics and education, multiculturalism Nous explorons ici la nature du savoir et de lʹéducation et comment la tradition islamique a modelé leur compréhension. Nous comparons les images contemporaines des écoles ʺstyle Talibanʺ qui sont faites de ʺpar coeurʺ et de litéralité dure et autoritaire avec certaines écoles dans lʹhistoire de lʹIslam qui affichent une plus grande générosité face au savoir. Nous établissons un lien entre ces notions et un livre illustré pour enfants récemment publié, une histoire vécue dʹun libraire de Basra, en Iraq, pendant la récente invasion américaine. Même si cette histoire est une ʺhistoire vraie ʺ, nous considérons sa valeur et nous pensons que les éducateurs pourraient accepter cette vérité ou même quʹils devraient lʹaccepter. Mots clés: philosophie de lʹIslam, la connaissance du curriculum, la formation du savoir, lʹéducation et les politiques, le multiculturalisme
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.047 | 0.047 |
| Scholarly communication | 0.020 | 0.016 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 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".