Expected effects of euro 2020 games for the economy and society in Azerbaijan
Bibliographic record
Abstract
Countries such as France, the USA, Great Britain or Greece are common names of host nations for some of the biggest global sport events, such as the Olympic Summer and Winter Games, the FIFA World Cup or UEFA European Championships (EUROs).During recent years, there is a trend that some of these mega events moved to comparably less developed or developing countries such as South Africa, Brazil, Russia or Azerbaijan. Azerbaijan started to host mega sport events in 2015 with hosting the 1st European Games. Since then, the country continued to host sport events such as regular Formula 1 races or the 4th Islamic Solidarity Games. In 2019, Baku hosted for the first time the final of the UEFA Europa League, a prestigious European club football competition. That game could be seen as a kind of dress rehearsal for the upcoming games of the EURO 2020. For the first time in history, the EURO will be staged in 12 different cities from all over Europe. Three group stage games as well as a quarter final will be played at Baku Olympic Stadium. This will be the biggest event so far that Azerbaijan took responsibility to host. Since the beginning of such types of sport mega events, policy makers and managers speculated about the potential beneficial effects of hosting such events for both the society in general and the (local) economy in particular.This study will contribute to the literature by investigating the potential effects of hosting EURO 2020 games in a developing country, i.e. Azerbaijan. The research question is: What are the expected effects of EURO 2020 games for the economy and society in Azerbaijan?
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".