Beyond Pain Narratives? Representing Loss and Practising Refusal at the Astoria Hotel
Bibliographic record
Abstract
Recent scholarship that contends with the ethical and political difficulty of representing loss has emphasized the importance of Indigenous, racialized, urban, and/or otherwise oppressed communities’ refusal to recite “pain narratives”. According to these formulations of refusal, there are some things about which the academy does not need to know. Not as much scholarship has considered that there may be some pain about which we do need to know. Drawing on archival records, nine months of ethnographic embeddedness in Vancouver’s Downtown Eastside (DTES), and conversations with two DTES organizers, this article responds to this question by arguing for representations of loss that cut against the spectacularity of pain narratives by rendering visible how loss manifests in the everyday. By way of example, this article contextualizes loss at a Single Room Occupancy (SRO) hotel called the Astoria Hotel. Tracing the history of the Astoria from the early 1900s to the present-day, this article shows how ongoing death, loss, and incumbent remembrance at the Astoria was not—and is still not—inevitable. It shows how DTES organizers, including those connected with the DTES SRO Collaborative and the Tenant Overdose Response Organizers, are working against violent forces which threaten the neighbourhood and community. In doing so, this article brings together studies of urban history, ethnography, and public memory to take seriously the notion that some pain does matter, while at the same time, inviting critique and further discussion of what it means to subvert pain narratives and what constitutes the work of refusal.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.025 | 0.044 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| 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".