Prospects for tourism and economic growth in North America in the context of the 2026 Fifa World Cup
Bibliographic record
Abstract
The 2026 FIFA World Cup is a major event with a significant global social and economic impact. The tournament is expected to boost host countries’ tourist flows, economic growth, and infrastructure development. In this context, the study of its impact on the economic and tourism sectors is particularly relevant. The subject of the study is the impact of the preparation and holding of the 2026 FIFA World Cup on the development of the tourism industry and the economy of North America ‒ the United States of America, Canada, and Mexico. The article is aimed at determining the prospects for tourism and economic growth in North America in terms of hosting the 2026 FIFA World Cup. The study’s objectives are to identify the main tourist benefits of the 2026 FIFA World Cup and to analyse the expected economic benefits of the upcoming world football championship, including for individual cities. To ensure the objectivity of the study, the following scientific methods were used: analysis, synthesis, structural and logical generalisation, and systematisation. The main results of the study. The study identified the key tourism benefits of the 2026 FIFA World Cup for North American countries, in particular in the context of growing international tourist flows and enhancing the attractiveness of destinations. In addition, the study analysed the expected economic benefits of the tournament, including the potential impact on the socio-economic development of individual cities, such as infrastructure investment, job creation, and increased local budget revenues. Conclusions. The 2026 FIFA World Cup offers unprecedented opportunities for economic stimulus through tourism. The influx of tourists will generate significant economic activity, benefiting various sectors such as hospitality, transport, and retail. The development of tourism will create new jobs, and financial flows from the tourism sector will help stimulate various sectors of the economy.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".