Imagining Spaces of Violence and Transgression in Vancouver and Northern Ireland
Bibliographic record
Abstract
In Chapter 5 we discussed responses to migration across four distinct regimes; the making of liminal in-between spaces in the form of camps for the displaced, in Bauman's terms ‘human waste’; and the usefulness of a threefold analysis of space through the relational, embodied and imagined experiences of migrants. In Chapter 6 we build on this analysis by examining the social dynamics and the visual and material culture of urban space as well as the embodied, relational and imagined/lived experience of public space in two cities, Vancouver, Canada and Belfast, Northern Ireland. We focus specifically on two areas of each city, the Downtown Eastside (DTES) in Vancouver, also known as ‘skid row’, and central and central/north Belfast, where the ‘peace lines’ and ‘peace walls’ separate, demarcate and act as borders between two communities of Belfast's citizens. In Chapter 6 we focus specifically on the issue of space, place, violence and transgression, drawing on these two case studies in Vancouver and Belfast. ‘Imagining spaces of violence and transgression in Vancouver and Northern Ireland’ focuses first of all on the lives of indigenous women and sex workers in Vancouver's DTES and then explores the ‘architecture of conflict’ in the ‘peace walls’, ‘peace lines’ or ‘border lines’ in Belfast in the context of spaces of war, violence and conflict. For 26 years on 14 February, Valentine's Day, women of the DTES have led a Memorial March through the city, stopping at the places and spaces where women were murdered or went missing. We draw on material from walking methods, photographs and interviews with women who attended the march in 2016 to examine spaces of past, present and future in their lives. Arts-based walking methods are also utilised that explore the peace walls, peace lines and interfaces as border spaces through sensory multi-modal research. We suggest, drawing on Ash Amin's work, that the ‘convivial commons’ we experienced walking with residents in the Downtown Eastside of Vancouver and Belfast offers a good example of what might be called on the one hand a ‘successful public space’ with various opportunities to participate in communal activity (Amin, 2006: 1), despite the media messages and public perceptions about the two sites, particularly the DTES. On the other hand, however, the material impact of poverty, austerity, violence (and sectarianism in Belfast) complicates this, with the impact of poverty more visually apparent in the DTES.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.027 | 0.025 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".