The business effects of mega-sporting events on host cities: an empirical view
Bibliographic record
Abstract
The study is an empirical view of the important issue of the business effects of the mega-sporting events (MSEs), like the Olympic Games, on which there are favorable and unfavorable views, the design of the study is to go through different views and find out the effects from knowing or knowledgeable persons of the event with the help of a sample of 155 respondents drawn randomly from across the continents in the form of opinions on the positive and negative effects of the MSEs through a questionnaire, containing questions on economic development, infrastructure development, environ¬ment, lifestyles, etc., and their favorable and unfavorable responses were elicited. The data collected have been analyzed in terms of the characteristics of respondents and their negative and positive responses on the Olympic and FIFA. The findings on the whole of study show that the hosting of the MSEs has positive effects on the economy and society of the host cities through the influx of tourists, infrastructure development, and image promotion of the country, among others, notwithstanding the certain drawbacks in terms of environmental disturbances, and some inconveniences to the locals.
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 | 0.002 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".