Bibliographic record
Abstract
Clare spent over a quarter of a century confined in lunatic asylums. He was placed first of all in the High Beach Asylum in Epping Forest in June 1837, paid for by a subscription fund. Eliza Emmerson gave five pounds. Clare’s occupation is listed as labourer first, and then poet: the story of his literary life once again in nutshell. This was a private asylum run by Matthew Allen according to new ideas on moral management, which will be explained during the course of the chapter. Clare escaped in 1841 and walked almost all the way back home. After a short break, he was then placed in the Northamptonshire General Lunatic Asylum, paid for by Earl Fitzwilliam. Although not strictly speaking a county asylum, this nevertheless had some affinities with such institutions. He served a ‘life sentence’ in asylums, and then some more. 1 His experiences, over this long period, will be related to other life stories of the allegedly insane sometimes told in their own words and sometimes told by the authorities. As mentioned right at the beginning of this book, no claim is being made that Clare either knew or was influenced by such stories. The argument is once again that Clare’s own story can be illuminated by other ones. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.029 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".