Bibliographic record
Abstract
The central argument of this dissertation is th at Canadian reality is conditioned by government data and their related infrastructures. Specifically, that Canadian geographical imaginations are strongly influenced by the Atlas of Canada and the Census of Canada. Both are long standing government institutions that inform government decision-making, and are normally considered to be objective and politically neutral. It is argued that they may also not be entirely politically neutral even though they may not be influenced by partisan politics, because social, technical and scientific institutions nuance objectivity. These institutions or infrastructures recede into the background of government operations, and although invisible, they shape how Canadian geography and society are imagined. Such geographical imaginations, it is argued, are important because they have real material and social effects. In particular, this dissertation empirically examines how the Atlas of Canada and the Census of Canada, as knowledge formation objects and as government representations, affect social and material reality and also normalize subjects. It is also demonstrated th at the Ian Hacking dynamic Looping Effect framework of ‘Making Up People’ is not only useful to the human sciences, but is also an effective methodology that geographers can adapt and apply to the study of ‘Making Up Spaces’ and geographical imaginations. His framework was adapted to the study of the six editions of the Atlas of Canada and the Census of Canada between 1871 and 2011. Furthermore, it is shown that the framework also helps structure the critical examination of discourse, in this case, Foucauldian gouvemementalité and the biopower of socio-techno-political systems such as a national atlas and census, which are inextricably embedded in a social, technical and scientific milieu. As objects they both reflect the dominant value system of their society and through daily actions, support the dominance of this value system. While it is people who produce these objects, the infrastructures th at operate in the background have technological momentum th at also influence actions. Based on the work of Bruno Latour, the Atlas and the Canadian census are proven to be inscriptions that are immutable and mobile, and as such, become actors in other settings. Therefore, the Atlas of Canada and the Census of Canada shape and are shaped by geographical imaginations.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.010 | 0.098 |
| Scholarly communication | 0.021 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| 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".