Rethinking the Great White North: race, nature, and the historical geographies of whiteness in Canada
Bibliographic record
Abstract
Introduction: Where Is Great White North? Spatializing History, Historicizing Whiteness / Andrew Baldwin, Laura Cameron, and Audrey Kobayashi Part 1: Identity and Knowledge 1 Phantasy in White in a World That Is Dead: Grey Owl and Whiteness of Surrogacy / Bruce Erickson 2 Indigenous Knowledge and History of Science, Race, and Colonial Authority in Northern Canada / Stephen Bocking 3 Cap Rouge Remembered? Whiteness, Scenery, and Memory in Cape Breton Highlands National Park / Catriona Sandilands Part 2: City Spaces 4 The Occult Relation between Man and Vegetable: Transcendentalism, Immigrants, and Park Planning in Toronto, c. 1900 / Phillip Gordon Mackintosh 5 SARS and Service Work: Infectious Disease and Racialization in Toronto / Claire Major and Roger Keil 6 Shimmering White Kelowna and Examination of Painless White Privilege in Hinterland of British Columbia / Luis L.M. Aguiar and Tina I.L. Marten Part 3: Arctic Journeys 7 Inscription, Innocence, and Invisibility: Early Contributions to Discursive Formation of North in Samuel Hearne's A Journey to Northern Ocean / Richard Milligan and Tyler McCreary 8 Copper Stories: Imaginative Geographies and Material Orderings of Central Canadian Arctic / Emilie Cameron Part 4: Native Land 9 Temagami's Tangled Wild: The Making of Race, Nature, and Nation in Early-Twentieth-Century Ontario / Jocelyn Thorpe 10 Resolving the Indian Land Question? Racial Rule and Reconciliation in British Columbia / Brian Egan 11 Changing Land Tenure, Defining Subjects: Neo-Liberalism and Property Regimes on Native Reserves / Jessica Dempsey, Kevin Gould, and Juanita Sundberg Interlocations Extremity: Theorizing from Margins / Kay Anderson Colonization: The Good, Bad, and Ugly / Sherene H. Razack Notes References Index
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.021 | 0.010 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".