Bibliographic record
Abstract
Although I've been asked to revisit Homo Aestheticus, my second book, I will begin by describing the laborious and uneven emergence of its predecessor, What Is Art For?, which was published four years earlier, in 1988.I began writing it when I was a housewife in Sri Lanka, in the middle 1970s.I had some ideas and wanted to see where they would go.Writing was a way of finding out.And write I did, over and over, over years and miles.I added to the book in 1977-78 in Oxford, where I had a six-month fellowship to do nothing but read in the Bodleian Library.Later versions ensued in 1978-79 in Nigeria, and in Papua New Guinea between 1982-84.Finding a publisher took more than five years.These were pre-email days, necessitating intercontinental airmail letters that took as long as three weeks each way to reach their destination.And publishers are not known for prompt replies.I found myself waiting as impatiently for the mailman each day as Radha and other women in Indian miniature paintings waited for their lover.Most publishers whom I queried did not even want to see the book.One editor looked at the manuscript and asked me to cut it by about a third.I obliged, but she could not obtain strong enough recommendations from readers and, after eighteen months of hope and dread, turned it down.Devastated, I put the careworn pages away for a
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".