(De)constructing nation and race along the Canadian Pacific Railway: First Nations and Chinese migrants in the colonial project
Bibliographic record
Abstract
Much has been written on the history of the Canadian Pacific Railway (CPR), but rarely are conversations regarding the experiences of First Nations and Chinese immigrants on the railway brought together. This thesis will analyze how First Nations and Chinese people in Western Canada experienced the construction of the railway and how, as racialized peoples, they were excluded from the original national mythology centered on the completion of a transcontinental railway. The seemingly benign symbol of a railway representing the nation continues the violence of naturalizing colonial, capitalist structures in the national landscape. A closer look at this history reveals the dispossession of Indigenous peoples, the destruction of their ways of life and incorporation of the capitalist economy—all processes that continues today. The history of the railway also reveals the place of Chinese immigrants as a distinct, racialized labour force in late-nineteenth-century Canada that reinforced and that was informed by the racial and economic interests of the national subject at an important time in the development of the nation and its national myths. Drawing on the insights of Manu Karuka’s Empire’s Tracks, this analysis situates the CPR as a tool of colonial, capitalist, countersovereignty.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.042 | 0.019 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| 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".