MétaCan
Menu
Back to cohort
Record W4237187945 · doi:10.4324/9780203413869-9

Tourism, migration and place advantage in the global cultural economy

2007· book-chapter· en· W4237187945 on OpenAlexaboutno aff
Chitralekha Rath

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicCultural Industries and Urban Development
Canadian institutionsnot available
Fundersnot available
KeywordsTourismEconomic geographyEconomyGeographyBusinessEconomicsArchaeology

Abstract

fetched live from OpenAlex

Tourism, migration and place are intricately linked. As early as the 1880s, it became fashionable for middle-class New Yorkers to go slumming or ‘rubbernecking’ in Chinatown (Lin 1998: 174), and in 1938 Vancouver ‘officially’ opened its Chinatown to tourism (Anderson 1988, 1995). In the 1970s, Melbourne courted the ‘Chinese quarter’ for its perceived distinctiveness and began sponsoring major redevelopment plans to boost such declining areas. Chinatown was selected as symbol of cultural diversity and object of civic pride and tourism (Anderson 1990). Many other places have followed suit, including San Francisco, whose Chinatown now ranks among the top five tourist attractions in a city where tourism is the number one industry.1

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.655
Threshold uncertainty score0.975

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.311
Teacher spread0.260 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations10
Published2007
Admission routes1
Has abstractyes

Explore more

Same topicCultural Industries and Urban DevelopmentFrench-language works237,207