Autonomous Agents and Destination Image Formation of an Olympic Host City: The Case of Sochi 2014
Bibliographic record
Abstract
Scant attention has been given to the role autonomous agents (i.e., politically contentious news reports) might play in the formation of destination images about Olympic host cities. This study analyzed content of travel blogs related to trips to Sochi during the 2014 Olympics. Our purpose was to explore destination images held by travelers to Sochi in the wake of contentions news reports about Russia’s lesbian, gay, bisexual, and transgender (LGBT) legislation. Both thematic and CATPAC (content analysis software) content analysis were employed to derive themes representing impressions and perceptions of Sochi. Findings focus primarily on images related to Sochi as a tourist destination and an Olympic host city. Bloggers’ impressions and perceptions did not seem to reflect autonomous agents related to LGBT laws in particular. However, bloggers did reflect on misleading news stories more generally, and subsequent images from travel experiences that did not match expectations set by these stories. Implications for marketers are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".