Review of Peter Tyler, Teresa of Avila: Doctor of the Soul , London: Bloomsbury, 2013. 223 pages
Bibliographic record
Abstract
Peter Tyler had two main purposes, which he lays out in the Introduction to his book, in undertaking to write on the life, context, and work of the 16th century Spanish Carmelite nun, monastic reformer, mystic, saint, Doctor of the Church, and, as he calls her in the book’s subtitle, doctor of the soul, Teresa of Avila: first, to place her, especially by the style of her writing, within the medieval tradition of ‘mystical theology’; second, to bring her into conversation with the post-modern world, in particular in light of a certain trend today of revisiting, in the face of the crisis of the ‘death of modernity’, the riches of the pre-modern era. The first of these purposes is accomplished in Parts One and Two of the book’s three Parts, while the second purpose is approached in Part Three.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.024 |
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".