The Power of Words at Mega-Event Opening Ceremonies
Bibliographic record
Abstract
This article analyzes the opening speech of John Furlong, CEO of the Vancouver Organizing Committee for the 2010 Olympic and Paralympic Winter Games, at the Vancouver 2010 Winter Olympic Games. The main idea here is the great importance of a speech at the opening ceremony of a mega-event. The characteristics of the speech were analyzed through the speaker's choice of words (figures of speech and rhetorical devices). The role of the speech was analyzed to show how the main event concept is promoted. The research is divided into two parts. The first presents a review of the literature and of other relevant resources such as video material and web pages as a basis for preparing the theoretical part of the article. In the second, more empirically oriented part of the article, we conducted a linguistic analysis involving an indepth discourse analyses of John Furlong's speech and the analyses of syntax, semantics, and pragmatics. Through the review of the literature and of other resources, we have established the importance of an additional Olympic theme along with the main sport-related theme. The empirical research that is based on the discourse analyses shows that, despite its brevity, John Furlong's speech stresses the theme and the values of the mega-event, and it is also highly communicative, incorporating the various significant semantics and pragmatics of a good speech: Brevity, simple words and sentences, colloquial language, relating to the audiences, employing stylistic and grammatical devices, appealing to the audience's feelings and emotions, and the delivery with appropriate rhythm, emphasis, and pauses. When conducting the research, we were unable to identify any other studies that have undertaken a detailed analysis of this speech. There are numerous analyses of how the Vancouver 2010 Winter Olympic Games were prepared and also staged. We have also found some that focused on the theme of the event, but none focused on how the message of the event was delivered to the general public and the residents of host communities. This strengthens the originality and importance of our research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".