Bibliographic record
Abstract
I first read Peter Lombard in early 2000, while researching for my master's thesis.It was not the Sentences but two of his sermons, and there was something about them that intrigued me.I came back to them during my first year as a doctoral student in Toronto, and they remain a central part of this book.The idea to write about Peter and his students came to me in the summer of 2001, when I was back in Dublin and cycling down Dundrum Road, just before it crosses the Dodder River into Milltown.The idea to focus on Peter's career and to make the work genuinely biographical developed over time.I owe thanks to a number of people.Brian Stock has been of great help in reading through the book and in discussing ideas and plans for it.It was a privilege to benefit from his knowledge of not only the twelfth century but also its intellectual heritage.He was always enthusiastic and encouraging about my work and provided illuminating comments.Isabelle Cochelin, Michael Herren, Seymour Phillips, and David Luscombe read early versions of some of the chapters and offered useful ideas and feedback.Joseph Goering did the same, and he was also involved in the latter stages.His affection for and understanding of the field was always evident.Drafts of the book as a whole were read by anonymous readers.They provided constructive suggestions for improvement and saved me from errors.In the summer of 2010, a time when a second pair of eyes was needed and appreciated, my father and Jackie Cowan read smaller portions of the text.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.036 |
| 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 | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.296 | 0.229 |
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".