Bibliographic record
Abstract
The Bookstore, home of delights and haunt of fancy. (Henry James, A Small Boy and Others , 1913) “Buying books would be a good thing,” quipped Schopenhauer, “if one could also buy the time to read them in.” In this, the misanthropic German revealed the dilemma for all book lovers: so many books, so little time. According to the US trade magazine Publishers Weekly , a quarter of a million books are published annually in the United States alone. Once you allow for language, genre, taste and luck, it's still possible to be intrigued or provoked by hundreds of titles every year. And then there are all the old books: from ancient works of Greek philosophy, Roman drama or Japanese religion, to last year's missed blockbuster. Unlike films or paintings, these works can't be enjoyed in an hour. We have to devote days and weeks to them. (This was the rationale behind T. S. Eliot's terse defence of poetry in the modern age: “It takes up less space.”) Put simply, literature is yet another forum for distraction; a chance to be waylaid by less valuable pursuits. With this in mind, I've written a few words on the more charming, profound or authoritative books and essays I've read for Distraction . Most titles are stocked in universities or public collections, but some are worth buying. A home library is a wonderful thing – it offers continuity, access and inspiration. Perhaps most importantly, it stands as an enduring emblem of our ambitions and experiences.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.024 | 0.023 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.187 | 0.102 |
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".