Bibliographic record
Abstract
In his book A Great and Noble Scheme (2005), historian John Mack Faragher explains that the 1755–63 Acadian deportation can be termed “ethnic cleansing,” following contemporary United Nations and scholarly usage of the expression. In this paper, instead of addressing the terms of contemporary UN or scholarship, as Faragher does, to see which terms can be used for the Acadian tragedy, I provide a terminological history of the Acadian tragedy. This terminological history studies the terms used for the Acadian tragedy since the eighteenth century until the early twentieth century and shows how they recur in the mid-twentieth century discussions on genocide. First, I investigate Edmund Burke’s treatment of the Acadian tragedy seen as resulting from acts of destroying, rooting out, and extirpating, and the Abbot Raynal using terms for the tragedy such as embarking, transporting, driving away (later deporting), destroying, perishing, depopulating, and, indirectly, crime against humanity. I also focus on the nineteenth-century account of the Acadian tragedy by Catherine Reed Williams, who expanded Raynal with notions of barbarity, banishment, expulsion, extermination, and moral shock, and on others after her who brought terms for Acadians such as annihilation, atrocity, evacuation, and clearing. Second, I highlight Acadian memory in the 1950s genocide debate in Washington (DC) and how the terminological history of the Acadian tragedy appears in a document Raphael Lemkin had on Acadians. I also explain how Lemkin, unknowingly or not, rearticulated in publications and texts and found in his research the terms already used for Acadians as he addressed his genocide concept.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.009 | 0.018 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".