Bibliographic record
Abstract
Beckett and story The English writer E. M. Forster admitted of the novel as a form: 'Yes - oh dear yes-the novel tells a story.' It is difficult to imagine many Irish novelists so regretting story as the basis of their craft. Indeed, the novelist and playwright Thomas Kilroy has perceptively observed that that chromosome of story, the anecdote, is in the DNA of the Irish fictional tradition from at least the end of the eighteenth century. He advises, with reference to Maria Edgeworth’s Castle Rackrent (1800): The distinctive characteristic of our ‘first’ novel, that which makes it what it is, is not so much its idea, revolutionary as that may be, as its imitation of the speaking voice engaged in the telling of a tale. The model will be exemplary for the reader who has read widely in Irish fiction: it is a voice heard over and over again, whatever its accent, a voice with a supreme confidence in its own histrionics, one that shares with its audience a shared ownership of the told tale and all that it implies: a taste for anecdote, an unshakeable belief in the value of human action, a belief that life may be adequately encapsulated into stories that require no reference, no qualifications beyond their own selves.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".