From Montreal to Moscow: The Legacy of the Anti-Apartheid Olympics on the Boycott of the Cold War Games (1976-1980)
Bibliographic record
Abstract
The International Olympic Committee confronted a contentious period between 1976-1980 that featured repeated threats to unified global sport. In 1976, African states withdrew from the Montreal Olympics protesting New Zealand’s rugby contacts with apartheid South Africa. Four years later in 1980, the United States and over a third of the world’s Olympic Committees withheld their athletes from the Moscow Games opposing the USSR’s invasion of Afghanistan. Both events threatened to split world sport apart, one over the issue of apartheid and the other along Cold War lines. Even though these were successive boycotts, historians have largely treated these events in isolation. This dissertation establishes the overlap between these two boycotts and how the African walkout in Montreal directly Soviet affected the preparations for the Moscow Olympics and influenced the US boycott of the 1980 Games. It argues that the context of the anti-apartheid campaign is necessary to understand the lead-up to the 1980 Olympics and frames 1976-1979 as a period of struggle between the Global South against the institutions and countries of the Global North, thus challenging dominant Cold War narratives surrounding the Moscow Olympic Games. Additionally, by focusing on Africa, a continent caught in the middle of the Global Cold War, it demonstrates how regional concerns about apartheid competed with Cold War understandings about the Olympics. Though the Cold War would overwhelm the decolonization struggle in sport in 1980 by forcing countries into a US vs. USSR binary, this dissertation examines how countries sought to navigate through this situation and proposed contending understandings of the boycott and non-alignment. The dissertation reframes the 1980 Olympic conversation by demonstrating how the anti-apartheid struggle influenced proceedings and argues for interpreting the Moscow Olympics as an important moment in the longer anti-apartheid struggle rather than isolating it within the Cold War crisis of 1980-1984.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".