Bibliographic record
Abstract
My interest in ʿAyn al-Quḍāt was first piqued in 2007, when I was a doctoral student at the University of Toronto.Captivated by ʿAyn al-Quḍāt's unique manner of expression, I began to think that his writings would make for an ideal PhD dissertation topic.Given my inexperience and naïveté, my academic advisory committee gently pointed to greener pastures.Their advice could not have been better.After graduating in 2009, it would take me another five years of training to be able to step into ʿAyn al-Quḍāt's world.Thanks to a series of grants and fellowships (see acknowledgments), I was freed from teaching and administrative responsibilities for several academic years.This large block of study time gave me the opportunity to carefully read and take copious notes on all of ʿAyn al-Quḍāt's writings, work out a translation method that would allow his style to faithfully come across in English, and translate and explain the most interesting passages.The result of these labors is the present book, which serves as an introduction to ʿAyn al-Quḍāt's spiritual and intellectual teachings.Having put aside
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.393 | 0.230 |
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".