Bibliographic record
Abstract
Abstract Indigenous resistance to colonization can intersect uncomfortably and often violently with a fight by workers to access Indigenous lands for extraction and jobs. Jobs have always been a literal frontier of settler colonial conflict because, simply put, colonization takes work. When immigrants began to settle through recruitment programmes en masse in Canada, they benefitted from a scale of colonial land seizure unknown anywhere else in the world at that time. The means by which to settle was the work—both required and provided—by corporations like the railroads, the Hudson’s Bay Company, and colonization enterprises. By the late 19th century, the market for wage labour on farms and in the central manufacturing regions was underway as industrialization took hold; the emergence of capitalism was born through its deep reliance on colonial land policy. For this reason, the political economy of colonialism can be studied through a long history of intersecting class formation and colonial land policy in Canada. We might call this dynamic the wages of settlement. Citation: Pasternak, Shiri, ‘Labour, Settler Colonialism, and Economies of Extraction: The Wages of Settlement’ (20 Mar. 2025), in Beverley Mullings (ed.), Labour and the Economy, in Meena Dhanda (ed.), Oxford Intersections: Racism by Context (Oxford, online edn., Oxford Academic, 20 Mar. 2025 -), https://doi.org/10.1093/9780198945246.003.0084, accessed [date].
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".