Tourism As Osmosis: The Role of Apology Diplomacy in Shaping Tourist Arrivals in The Philippines
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".