A Quantitative and Comparative Approach to Royalist and Whig Sources in Hume’s History of England
Bibliographic record
Abstract
Abstract: David Hume’s History of England was repeatedly examined as a political project from one side or the other of the Whig—Tory divide, both by Hume’s contemporaries, and later historians. Recent scholarship has taken a more nuanced approach to the question of Hume’s partisanship or impartiality, and we join in by showing how modern computational methods can add to this discussion. This paper quantifies sources used by David Hume in his History of England : We applied computational methods to detect 347,323 instances of almost verbatim repeating passages, henceforth “reuse,” of different published texts in Hume’s History of England , which we then qualified, clustered, and compared. The aim was to test previous claims about Hume’s Tory and Royalist bias against evidence concerning his use of historical sources. We focus particularly on Royalist sources in his description of Charles I and his time. Having compared Hume’s use of previously published historical texts to Rapin’s, Carte’s, and Guthrie’s histories, we conclude that claims made by close contemporaries concerning his extensive reliance on Royalist sources are largely overstated. In addition, we suggest that Okie’s and MacGillivray’s later influential arguments about Hume’s Tory bias based on his use of sources are not justified. There are, therefore, good reasons to take Hume’s own claims about his attempt to be impartial seriously, but the situation therein appears altogether more complicated. Finally, we will show how this endeavor, the original aim of which was to assess the accuracy of claims regarding Hume’s political bias, has provided deeper insights into the methodologies of source utilization, evolving quoting practices, and intertextuality within eighteenth-century historiography.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.023 | 0.026 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".