SWEEPING THE SOFT POWER PODIUM: A QUANTITATIVE AND QUALITATIVE ANALYSIS OF OLYMPIC SOFT POWER'S IMPACT ON THE HOST NATION’S INTERNATIONAL IMAGE
Bibliographic record
Abstract
In February 2022, China will host the Winter Olympics in Beijing. During historical periods of international tension, nations use these types of mega-events as an extension of geopolitical competition to exercise soft power strategies and advance national interests. This thesis analyzes four Olympic case studies (2008 Beijing, 2010 Vancouver, 2012 London, and 2014 Sochi), using public international favorability polling to explore how Olympic hosts influence global perceptions and determine the measurable effects. Quantitative analysis of these factors reveals a strong correlation between increased international favorability ratings and hosting the Olympics, particularly when compared to non-Olympic hosted years. Considering the 2022 Beijing Winter Olympics, the 2028 Los Angeles Summer Olympics, and future mega-event bids, this thesis provides associated recommendations to support the U.S. national and defense strategy shift toward strategic competition. These suggestions focus on sports diplomacy; promoting the culture and values of the allied host nation vice host city; publicly refuting an adversary host's false strategic narrative via media and government channels; and solutions to increase allied Olympic bidding that mitigate historical adverse financial, social, and environmental effects. Lastly, the thesis provides a metric to track and analyze mega-event soft power effects to shape future strategy.
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 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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".