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Record W4401554569 · doi:10.1177/17506980241270873

Conjuring the ‘ship of dreams’: Spatial narratives and making the absent present around and within Titanic Belfast

2024· article· en· W4401554569 on OpenAlexaff
Jason Grek‐Martin

Bibliographic record

VenueMemory Studies · 2024
Typearticle
Languageen
FieldPsychology
TopicMemory, Trauma, and Commemoration
Canadian institutionsSaint Mary's University
Fundersnot available
KeywordsNarrativeWonderHistoryNorthern irelandFeelingVisual artsTragedy (event)Identity (music)AestheticsArtLiteraturePsychologyEthnology

Abstract

fetched live from OpenAlex

Powerful prosthetic memories of the Titanic story have circulated in popular culture for more than a century and have become the focus of several experiential museums and Titanic-focused heritage sites on both sides of the Atlantic, most notably in Belfast, Northern Ireland, where the doomed liner was built. Drawing on recent field work, this article analyses the ways in which a series of engaging, multisensory and three-dimensional spatial narratives have been deployed within the new Titanic Belfast signature attraction and the surrounding memoryscape to make the absent ship present for visitors once more. These spatial narratives inform an affective heritage approach that focuses, not on the tragedy of Titanic’s sinking, but on feelings of awe and wonder at the scale and grandeur of the great ship, allowing memory managers to tell a celebratory, Belfast-focused origin story for this internationally renowned ‘ship of dreams’.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.030
Scholarly communication0.0080.007
Open science0.0020.012
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.078
GPT teacher head0.359
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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