Bibliographic record
Abstract
Abstract John J. MacAloon's eloquent advocacy of intercultural communication, exchange, and education in Olympic sports echoes and brings contemporary relevance to the internationalism of Pierre de Coubertin. MacAloon argues that the ultimate test of the Olympic Movement should be the way that during its diverse activities, it enables athletes, coaches, game officials, decision-makers, journalists, spectators, and citizens of host cities and countries to leave their comfort zones to engage with and learn from those from other cultures. In the process, he believes, they will gain confidence in speaking out against xenophobia and hatred, no mean contribution in an increasingly divisive world. This personal reflection upon MacAloon's work outlines the core of his ideas as he first presented them in the 1980s and how they were interpreted at the time; recounts the innovative and illuminating ways of his ethnographical research on the road of Asian and Olympic torch relays, at Olympic and Winter Olympic Games, and his field trips with colleagues, activists, and Olympic insiders at colloquia and conferences; and discusses the possibilities and challenges of intercultural education in Olympic sport today, placing them in the context of the recent calls for a boycott of the 2022 Winter Olympics in Beijing.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".