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Record W7117468708 · doi:10.15575/jassr.v7i2.166

Tourism As Osmosis: The Role of Apology Diplomacy in Shaping Tourist Arrivals in The Philippines

2025· article· en· W7117468708 on OpenAlexaboutno aff
Noel Yee Sinco

Bibliographic record

VenueJournal of Asian Social Science Research · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsTourismDiplomacySoft powerPower (physics)Government (linguistics)PoliticsSymbol (formal)

Abstract

fetched live from OpenAlex

Abstract This paper examines how apology diplomacy influences foreign tourist arrivals to the Philippines from 2008 to 2025, introducing an innovative framework that views tourism as osmotic. Using a descriptive interrupted Time-Series Analysis (ITSA) of the Department of Tourism Data, the study analyzes key crises such as the 2010 Hong Kong hostage crisis, 2012 Scarborough Shoal Standoff, 2013 Taiwan fishermen shooting incident, the Canadian Garbage Crisis, the Tubbataha Reef Incident and other crises mentioned. While South Korea becomes a neutral (control) variable to analyze the fact that even if having a similar garbage crisis like Canada`s, it has strong tourism relations with the Philippines. It determines how political crises like these affect tourism recovery. Findings show that prompt and sincere public apologies function as a valve that restores tourist flows and repairs damaged bilateral relations, while unapologetic or delayed responses prolong travel bans and negative perceptions. The research concludes that apology diplomacy is a vital soft power instrument, translating symbolic gestures into economic and reputational gains. The Department of Tourism is recommended to implement this tourism risk management mechanisms in case of these kinds of crisis and not only the previous health pandemics (COVID or SARS). This osmosis model of tourism bridges diplomacy, development, and nation branding in the post-crisis context.

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)0.018
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.912

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.082
GPT teacher head0.493
Teacher spread0.411 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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