Spectacle, spectrality, and the everyday : settler colonialism, Aboriginal alterity, and inclusion in Vancouver
Bibliographic record
Abstract
This dissertation examines everyday social relations in the settler colonial city of Vancouver. Its contemporary ethnographic focus updates and reworks historical and political analyses that currently comprise the growing body of scholarship on settler colonialism as a distinct socio-political phenomenon. I investigate how non-Aboriginal residents construct and relate to Aboriginal alterity. The study is situated in three ethnographic sites, united by their emphasis on “including” the Aboriginal Other: (1) the 2010 Winter Olympics, which featured high-profile forms of Aboriginal participation (and protest); (2) the Mount Pleasant public library branch, which displays a prominent Aboriginal collection and whose staff works closely with the urban Aboriginal community; and (3) BladeRunners, an inner-city construction program that trains and places Aboriginal street youth in the local construction industry. Participants in this research include non-Aboriginal “inclusion workers” as well as non-Aboriginal patrons at the library, construction workers on a BladeRunners construction placement site, and audiences at Aboriginal Olympic events. I explore how my participants’ affective knowledges shape and are shaped by spatial and racializing processes in the emergent settler colonial present. My analysis reveals how everyday encounters with Aboriginal alterity are produced and experienced through spectacular representations and spectral (or haunting) Aboriginal presence, absence, and possibility in the city. In relation to inclusion initiatives, I argue that discourses of Aboriginal inclusion work to manage and circumscribe Aboriginal difference even as they enable interaction across difference. Ultimately, I suggest that social projects aimed at addressing Aboriginal marginality and recognition must actively engage with and critique non-Aboriginal ideologies, discourses, and practices around racialization, meaning-making, and settler privilege, while working within and against a spectacular and spectralized milieu. This research demonstrates how critical ethnography can be leveraged productively to analyse settler participation in the reproduction and transformation of the colonial project.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.014 |
| Scholarly communication | 0.010 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".