Bibliographic record
Abstract
When V. S. Naipaul writes his life story, a subject to which he compulsively returns, that story always has two cornerstones: how he came to be a writer and his father. The two are intimately related: Naipaul, looking back, marvels at how far the boy in colonial Trinidad had to travel in order to become the published writer, and he considers his literary ambition to be his greatest legacy from his father, Seepersad, who had been a journalist and had published a collection of short stories. The son even received his original subject matter from his father, who had urged him to take his, Seepersad's, life as the theme for a novel. That novel became A House for Mr Biswas (1961), which tells how a man utterly without a story nevertheless fashioned his life into a story by making his son into the writer who could write it. Naipaul's novel, like Soyinka's, is a son's fictional portrait of his father. The two fathers are almost exact contemporaries: in 1938 Mr Biswas is 33, Akinyode Soditan 32, and both have infant children. Both live their entire lives in a British colony, receive a colonial education, and are defined by their relation to the world on paper. Their experience of the world of writing is that, as it widens their horizons, it threatens them with a sense of personal irrelevance.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".