Bibliographic record
Abstract
2009), Washington University in st louis (2009), University of Michigan at ann arbor (2009), University of Texas at austin (2009) and the University of Geneva (2009).similarly, feedback from participants at the following conferences was invaluable: association Portugaise d'Etudes Françaises (Porto, 2006), 'The Persistence of Philology' (Toronto, 2007), Medieval academy (Toronto, 2007), american comparative literature association (long Beach, 2008), 'The spaces and locations of Medieval culture' (london, 2009), The canadian society of Medievalists (Montreal, 2010) and 'The Ethics of Translation' (london, 2010).rather than name all the individuals who kindly invited me to speak on these occasions and the many who listened attentively, asked such useful questions and, on occasion, shared their own work with me, I trust they will all recognize themselves in this list and accept my most cordial and grateful thanks.My colleagues in the French Department in King's college london make it an unparalleled environment in which to teach and think.I have also benefited hugely from working with our outstanding recent and present graduate students and would in particular like to mention camilla Barker, Gillian Ni cheallaigh, Tom Hinton, richard Mason, Hannah Morcos, David Murray, Jessica stacey, Jessica stoll and luke sunderland, all of whom have contributed to my thinking in this book.Jessica stoll did heroic work on the index for which I am eternally grateful.In King's special thanks are also due to Jan Palmowski, who engineered study leave when I needed it and whose unwavering support has been invaluable.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.389 | 0.173 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".