Patrick Brantlinger (1990), ‘How Oliver Twist Learned to Read, and What He Read’, in Patrick Scott and Pauline Fletcher (eds), Culture and Education in Victorian England, papers from the Bucknell Review, London and Toronto: Associated University Presses, pp. 59-81
Bibliographic record
Abstract
Perhaps it’s Oliver’s good fortune that, discounting Fagin’s anti school for pickpockets, he never attends any school. Throughout Dickens’s novels, schools are places of tyranny and miseducation. Noah Claypole has been to a charity school, which seems to have taught him nothing but cruelty and low cunning; though appar ently better educated than Oliver, Noah, alias “Morris Bolter,” joins the criminals. But maybe Dickens says nothing about Oliver’s schooling because he takes it for granted. In fact there were schools for pauper children in the late 1830s. Even under the Old Poor Law a pauper schoolmaster might teach in a parish workhouse-a kindly old man fills that role in the first o f the Sketches by Boz. But it was more often the case, as one historian of “schools for the people” wrote in 1871, that “the only sort of information which the [workhouse] young had to interest them, was a rehearsal o f the exciting deeds o f the poacher and the smuggler, or the . . . adventures of abandoned females.” 1 On the other hand, the Benthamite drafters o f the New Poor Law o f 1834 stressed education as the key to eliminating pauperism .2 Yet well into the 1840s little progress was made toward providing adequate workhouse schools. Qualified teachers were nonexist ent, salaries rock-bottom, and classroom conditions wretched.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.012 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.007 |
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".