Bibliographic record
Abstract
This dissertation asks how environmental information about the Canadian northwest was gathered, transmitted, and stored in the post-Confederation period (1867-present). It pays particular attention to the way objects such as photographs, maps, images, documents, and other material objects were employed to overcome the disparate geography of settlement. My key argument is that producing a unified model of Canada depended on employing both objects able to convey landscapes and subjects able to decode them geographically. To demonstrate this claim, the dissertation provides an interpretive method for studying the historical geography of Canada called spatial history, which I employ in two ways. The first argues that various actors and institutions worked to tie European newcomers to the land by entwining historical and geographical knowledge of Canada. I emphasize the cooperation between archives, government bureaus, and schools and universities in fashioning ‘spatial histories’ of modern Canada. The second focuses on how objects were used to transmit knowledge between different these different scales and sites. Here I show how the ‘spatial histories’ told by objects required users to adopt new means of seeing and interpreting landscapes, and in turn adopt new understandings of self, citizenship, and belonging. The case studies that make up the dissertation are joined by a set of themes that resonate in the spatial history of Canada: archives, visualization, environmental knowledge, state formation, the history of Canadian geography, historical commemoration, public memory, and regionalism.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.029 | 0.029 |
| Scholarly communication | 0.017 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".