What’s in a name? : Scottish settlement and land plot names and settler colonialism in nineteenth century Inverness County, Cape Breton
Bibliographic record
Abstract
The application of place names by Scottish colonizers is a well-studied field. However, those studies focus on the identification and classification of such names, with little emphasis on how these names actually came to exist. This thesis provides an in-depth analysis of those that exist in Inverness County, exploring two types of names: those applied to settlements, settlement names; and those applied by individuals to land granted them, land plot names. Through analysis of land petitions, maps, and post office records, this thesis charts the settlement of places that would come to have Scottish names and the emergence of Scottish settlement and land plot names within Inverness County to demonstrate that these names were introduced as a result of large-scale Scottish settlement. This contrasts with the place names that can be found in other parts of the former British Empire such as Australia, New Zealand and even other parts of Canada where Scottish names came to exist as a result of Scottish colonial involvement as administrators, explorers and cartographers. While both can be seen as part of the process of settler colonisation; the replacement of the Indigenous population, in the case of Inverness County, the Mi'kmaq, with an exogenous one, the Scots, the evidence that has been considered demonstrates that the names in Inverness County are distinct in the sense that they were introduced by the Scots settlers themselves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".