Have the Olympic Games become more migratory? A comparative historical perspective
Bibliographic record
Abstract
It is often believed that the Olympic Games have become more migratory. The number of Olympic athletes representing countries in which they weren't born is thought to be on the rise. It should, however, be noted that migration in the context of sports is hardly a new phenomenon. In this paper we hypothesise that, as a reflection of global migration patterns and trends, the number of foreign-born Olympians hasn't necessarily increased in all countries. Furthermore, it was expected that the direction of Olympic migration has changed and that foreign athletes increasingly come from a more diverse palette of countries. We conducted an analysis of approximately 40,000 participants from 11 countries who participated in the Summer Games between 1948 and 2012. The selected countries have different histories of migration and cover the distinction between 'nations of immigrants' (Australia, Canada, United States), 'countries of immigration' (France, Great Britain, Netherlands, Sweden), 'latecomers to immigration' (Italy, Spain) and, what we coin, 'former countries of immigration' (Argentina, Brazil). We conclude that the Olympic Games indeed have not become inherently more migratory. Rather, the direction of Olympic migration has changed and most teams have become more diverse. Olympic migration is thus primarily a reflection of global migration patterns instead of a discontinuity with the past.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".