Heritage trails and the framing of place authenticity in Belfast’s <i>Titanic</i> memoryscape
Bibliographic record
Abstract
Titanic heritage tourism has recently become a major objective in Belfast, Northern Ireland, as civic leaders look to leverage the infamous liner’s local origins to advance the city’s post-industrial, post-conflict economic development. Much of this activity has centred on the Titanic Quarter, a new residential, commercial, and cultural district emerging in the historic docklands of East Belfast. This district is home to Titanic Belfast, an ostentatious, new-build visitor attraction created to tell a celebratory, Belfast-focused Titanic origin story. Titanic Belfast is surrounded by numerous heritage elements once associated with the Harland and Wolff shipyards where Titanic was built, which have been incorporated into two heritage trails traversing the Titanic Quarter. This article draws on literature related to authenticity, heritage trails, and memoryscapes to interrogate how these trails underscore the place authenticity of the heritage assets dispersed throughout the Titanic Quarter. It also examines how these trails position Titanic Belfast as the focal point of a vast and coherent maritime heritage memoryscape, revealing a symbiotic relationship whereby Titanic Belfast draws people and capital to the area while the authentic heritage elements nearby furnish the ‘credibility armour’ needed to justify this grandiose new signature attraction and the celebratory narrative it offers visitors.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".