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Record W2340177564 · doi:10.5539/ijps.v8n2p76

Place Identity: How Tourism Changes Our Destination

2016· article· en· W2340177564 on OpenAlexvenueno aff
Yi Liu, Jieyu Cheng

Bibliographic record

VenueInternational Journal of Psychological Studies · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicPlace Attachment and Urban Studies
Canadian institutionsnot available
Fundersnot available
KeywordsTourismSocial identity theoryIdentity (music)Identification (biology)Social identity approachSocial groupSpace (punctuation)Social psychologyGroup (periodic table)PsychologyChinaBoundary (topology)Social spaceSociologyAestheticsPolitical scienceLawPhilosophy

Abstract

fetched live from OpenAlex

According to the social identity theory, once people have categorized themselves and others into different group, they will contrast themselves and others, and their thinking and behaviors will become bounded up with in-group membership. There will be an emotional significance to our identification with a group, when outsiders come into a destination, indigenes will find the differences between the outsiders and themselves, then divide them into different groups that can reinforce the identification about their group even awake and strengthen place identity. Based on social identity theory and the comparative case study of Lijiang (a world culture heritage in China) and Palma (a tourist island in Spain), this essay is going to explain how tourism awakes place identity and affects identity boundary which causes a series phenomena that happened in our daily life no matter where we are, such as culture recover, maintaining the link with space, in-group favoritism, out-group bias and conflicts.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.008
Scholarly communication0.0090.004
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.159
GPT teacher head0.483
Teacher spread0.324 · 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

Citations13
Published2016
Admission routes1
Has abstractyes

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