“All the nightmare images of ethnic conflict in the twentieth century are here”: Erroneous statistical proofs and the search for ethnic violence in revolutionary Ireland, 1917–1923
Bibliographic record
Abstract
Abstract In the 1990s, Canadian historian Peter Hart claimed tens of thousands of native southern Irish Protestants experienced something akin to “ethnic cleansing” at the hands of the IRA in the early 1920s. Hart's research revised the “Irish nationalist revolution” (ca. 1917–1923) as an essentially ethnic conflict, and this article re‐examines his evidence and methodology. Exaggerating the number of forced migrations, Hart's analysis rests on erroneous statistical proofs which he supported with a gross evidence selection bias. To better understand Hart's revision, his work is compared with Michael A. Bellesiles' Arming America: The Origins of a National Gun Culture (2000), which also exhibited similar but also very different statistical errors. A central argument in this article is that erroneous statistical proofs are best understood as social constructs, where they articulate the prejudices of their host academies. Greater awareness of this problem is needed if it is to be avoided in the future.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".