Most of our country is wild and unspoiled : advertising gender, race, and empire for Western Canada, 1867-1911
Bibliographic record
Abstract
Immigration handbooks published by British, Canadian, and provincial institutions at the close of the nineteenth century were designed to encourage resettlement and constitute one aspect of the large scale immigration campaigns embarked upon by the federal government to colonize Western Canada. This thesis utilizes these handbooks in order to rethink not only the advertising campaigns of the Canadian government, but also to reconsider the interconnectedness of Canada and Great Britain's imperial pasts. Although they were produced to encourage migration from metropole to colony, immigration handbooks, this thesis argues, became the lens through which Canada's reverse imperial gaze was cast. Because immigration handbooks simultaneously reflected and constituted the imperial world of which they were a part, they provided intending immigrants with information not only about Western Canada, but also about the empire writ large. Moreover, these handbooks suggest how metropolitan ideals, colonial realities and the tensions that arose in-between were understood, maintained, and refracted by the peripheries. Immigration handbooks provided intending immigrants with practical and useful information, while simultaneously carving out the gender and racial ideals that were deemed appropriate for this edge of the British Empire.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.013 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".