An investigation into how the Olympic Games have impacted upon their host countries; focusing mainly the economic footprint that the Games leave behind.
Bibliographic record
Abstract
The aim of this research project is to explore the different ways in which the Olympic Games affect their host countries, whether that is by bringing in foreign direct investment, providing jobs, improving infrastructure, or on the flip side, by causing unnecessary uproar such as providing the country with huge costs, unused state of the arts sporting venues once the Games have finished and taking funding from causes that may be deemed more deserving such as local hospitals, arts councils and education.\n\nThe project will focus on five major Olympic Games where two are to have had advantageous consequences for the host countries, these Games will be the 1984 Los Angeles Games and the 1992 Barcelona Games. Two of the Games to be explored are considered to have not been economically beneficial for the cities. These will be the 1972 Munich Games and the 1976 Montreal Games and the final Games to be examined and compared to the others will be the 1980 Moscow Games, to compare a mega event that occurred in a communist country, to examine if this may have had any differing impact on the outcome. The project will then attempt to research and explain the differences in outcomes of these Games. Then there will be a final section focusing on two future Games. Included will be their current status, what has been done to date and what we can expect in the upcoming months and years. The penultimate Games to be examined will be the Beijing Olympics, with main emphasis on the 1 year count down and progress, trials and tribulations they face and the media attention it receives. The final Games will be London and will include the current political debates and reaction to hosting the Games in our home city. This information will all be taken from current newspaper articles, the news, radio and research, and will be different from the information gathered for the other Games, as each of the Games are in different stages of their process. This is due to the true cost of the games not being finalised until long after the Games have ended. For example, the cost of the 2004 Athens Olympics is still, 3 years on, rising. Thus providing reason why the project must focus mainly on Games that have been finished for at least a decade.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".