Bibliographic record
Abstract
Writing a book about obligations necessarily involves the accumulation of new responsibilities, new debts, new obligations.Anyone who knows me well will know that Romantic Hospitality is indebted, first and foremost, to David L. Clark.A gifted scholar, the most giving of mentors, David continues to show me a generosity that far exceeds any possibility of restitution on my part.The irresistible pull of an inadequate economy of exchange nevertheless compels me to say to him, at the very least and with the deepest sincerity, "thank you."I am also compelled to thank a number of others who have helped, each in their own way, to make this book possible.They include Reeve Parker (for good conversation and the use of his office); Tilottama Rajan (for her encouragement and tough questions); Nicholas Halmi (for letters, references, and clearing up questions of translation); Grace Kehler and the late Sylvia Bowerbank (for asking me to think otherwise); Dana Hollander and Patrick J. Ryan (for their many helpful comments and suggestions); George Grinnell and Murray J.Evans (for always having ears to bend); Matt Kavanagh and Jake Kennedy (for much-needed rounds at Chedoke); the entire nassr community (for invaluable feedback over the years); the readers and editorial staff of the Wilfrid
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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.551 | 0.407 |
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".