The 1846 Repeal of the Corn Laws: Insights from a Classification Tree Approach
Bibliographic record
Abstract
Prime Minister Robert Peel was forced to resign in 1846 over the Repeal of the Corn Laws. Far from being a relatively unimportant piece of agricultural legislation, the Corn Laws, and their continuance, formed part of the ideology of the Conservative Party of the time. By proposing to Repeal the Corn Laws, Sir Robert was attacking the beliefs on which his party had won victory in the 1841 General Election. The result was a serious split within the Conservative Party over the Corn Laws. The majority of Conservatives voted against their own government, while 114 ‘Peelite’ Conservatives voted with Peel and the government. Why those particular Conservative Members decided to split away from their colleagues has been the subject of a large amount of research, mostly with ‘demand-side’ models which assume that the MP is little more than a mouthpiece for constituency interests. Peel's 1845 motion, a year before Repeal, to increase the yearly grant to the Irish Catholic seminary at Maynooth created very large controversy, and a backbench rebellion in which half of his own party voted against the government. As with Repeal, Maynooth passed only because the Opposition party decided to side with the government. This article uses principal component analysis and a classification tree analysis for the first time to show that while Conservative Members were voting with constituency interests in mind, their previous voting record over Maynooth is an overlooked and important predictor.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".