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Record W2083982862 · doi:10.1080/1743873x.2013.799172

Remembering the Beatles' legacy in Hamburg's problematic tourism strategy

2013· article· en· W2083982862 on OpenAlexaboutno aff
Stephanie Fremaux, Mark Fremaux

Bibliographic record

VenueJournal of Heritage Tourism · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicMusic History and Culture
Canadian institutionsnot available
FundersCity of Melbourne
KeywordsTourismQuarter (Canadian coin)MusicalIdeologyCultural heritageInvestment (military)Music festivalEconomyHistoryPolitical scienceGeographyVisual artsArchaeologyArtPoliticsLawEconomics

Abstract

fetched live from OpenAlex

Since the late 1980s/early 1990s, Liverpool has been synonymous with Beatles-related tourism, investing in museums, city walks, and redeveloping the Mathew Street Cavern Quarter. Another, perhaps lesser known, site of Beatles' tourism that has slowly immerged in recent years is the Reeperbahn area of Hamburg, Germany. While a number of cities with a strong musical heritage have developed tourism and urban regeneration around their musical past, primary research and photographic evidence gathered in Hamburg reveals that Hamburg is a city of conflicting identities. The city's leaders want Hamburg to compete as a cultural and financial site of tourism and investment on a global scale. However, by examining the mytholization of ‘the Beatles’ Hamburg’ at the Beatlemania Museum, and the lack of investment in the surrounding infrastructure, research shows that this act of selective memory is driven by economic and ideological agendas in Hamburg's overall urban regeneration plans. Arguably, the multi-billion euro HafenCity project is to be the new vision and focus of Hamburg's regenerated image. This article does not argue for a ‘Disneyfication’ of Hamburg's Reeperbahn area, but attempts to highlight the missed opportunities for the city to support and cultivate its music heritage and struggling artisan/independent scene.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0130.000

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 head0.024
GPT teacher head0.209
Teacher spread0.185 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations15
Published2013
Admission routes1
Has abstractyes

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