Compiling Knowledge, Enacting Space, Binding Time: Innis’s Canadian North (1928–1944)
Bibliographic record
Abstract
While Harold A. Innis’s contributions to media theory and economic history are well recognized, his attention to Canada’s North has received less notice. This paper brings together and analyzes Innis’s most sustained engagement with the North: his sixteen-year project of reviewing more than 150 books about the region for the Canadian Historical Review. By examining Innis’s North as a mediated text, the paper traces how his reviews were informed by concerns with enacting space and binding time. The paper discusses how Innis used his reviews to compile information about a largely unknown area, map its natural-resource potential, and commemorate its colourful and sometimes-turbulent past. Innis contributed to a construction of the region as a last frontier whose conquest could unite Canadians in common purpose and identity. Innis’s reviews provided a platform for him to criticize the federal government’s policies on the region, but his contributions to our understanding of the region largely failed to reflect the complexities of an already occupied, already changing North. Nevertheless, his discussions of political and economic development in the North anticipated current discussions of how Canada’s northern tier is undergoing transformation through the exploitation of resources and climate change.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.027 | 0.022 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".