“One message, all the time and in every way”: Spatial subjectivities and pedagogies of citizenship
Bibliographic record
Abstract
What are the pedagogical encounters through which we learn about hierarchies of citizenship and the positions to which we belong in a nation? In this article, we seek to answer this question by examining the ways Muslim and non-Muslim bodies are spatially related to the settler nation-state of Canada, to reveal how outsider subjectivities are constructed and maintained. We articulate the ways spatiality and subjectivity are intertwined with how normative and non-normative citizenship is learned. This relationship is examined through the events of the Parliament Hill shooting in Ottawa in 2014, and the subsequent state funeral held in the city where the authors live. We argue that these events were explicitly pedagogical and demonstrate the ways spatial subjectivities are produced along the racial lines of the nation. We trace how spatiality and subjectivity are interwoven in conceptions of Canadian citizenship, how these relationships prioritize the maintenance of a normative white settler citizenship identity, and we highlight this process in the pedagogical nature of the War Memorial and the subjectivity it calls forth. We define what we see as pedagogies of citizenship and analyse the subsequent state funeral and procession through our own lived experiences of the funeral's spatial imperative of subjectivity. We take up how the funeral as pedagogy asserted explicit Anglo-colonial power and its necessary constructs of embodied emplacement and settler futurity. Throughout, we consider how Anglo-dominance rests on multiple oppressions.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.074 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".