Walking, storytelling and melancholy survivals: memorialization of the ‘Troubles’ in Belfast’s City Centre
Bibliographic record
Abstract
This article explores the role of walking and storytelling as a mode of memory-making in Belfast’s City Centre, a ‘shared space’ that has largely been emptied of reminders of the 30-year conflict in Northern Ireland known as the ‘Troubles’. Memorialization remains a divisive and contentious process in Northern Ireland with two opposing narrative traditions and a lack of shared collective memory. In the absence of state-led and officialized memorials to the ‘Troubles’, I explore how urban heritage can be expressed in motion, through spatial stories told by place-based professionals (urban planners, architects, heritage practitioners, arts and community groups) in the City Centre. In particular, I employ David Lloyd’s idea of ‘melancholy survivals’ to describe the ways in which memories of conflict persist in the narratives we tell and in the small physical residues scattered throughout the City Centre, which we encounter through walking and spatial stories. I argue that walking go-along interviews with place-based professionals elicits storytelling that evokes a mobile mode of memorialization. I begin by discussing the context of memorialization in Belfast’s City Centre, its role during the ‘Troubles’, and its subsequent urban redevelopment as a ‘shared space’. I then map out critical discussions around my methodological framework, which considers spatial storytelling, geographies of affect and walking methods as ways to engage urban heritage in cities that have experienced conflict. This is followed by observations from the walking go-along interviews, which include stories of physical residues, the psychosomatic legacies of conflict and ways the difficult memories factor into narratives in Belfast’s City Centre.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.035 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".