The dreadful monster and its poor relations: taxing, spending and the United Kingdom, 1707-2021
Bibliographic record
Abstract
It has always been an important part of British self-image to see the United Kingdom as an ancient, organic and sensibly managed place, in striking contrast to the convulsions of other European countries. To a limited degree this is true, but, as Julian Hoppit makes clear in this fascinating and surprising book, beneath the complacent surface the United Kingdom has in fact been in a constant, often very tense argument with itself about how it should be run and, most significantly, who should pay for what. \n \nThe book takes its argument from an eighteenth century cartoon which shows the central state as the 'Dreadful Monster', gorging itself at the dinner table on all the taxes it can grab. Meanwhile the 'Poor Relations' - Scotland, Wales and Ireland, both poor because of tax but also poor in the sense of needing special treatment - are viewed in London as an endless 'drain on the state'. With drastically different levels of prosperity, population, industry, agriculture and accessibility between the United Kingdom's different nations, what is a fair basis for paying for the state?
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".