Sports as an Instrument of Foreign Policy Under the Military Rule in Nigeria: 1976-1996
Bibliographic record
Abstract
The importance and popularity of sports among the nations of the world is huge. Sports are among the few common denominators for all the nations of the world irrespective of their respective political ideologies and religious inclinations. Through sports, enemies in political and ideological realms can compete between and among each other in an atmosphere of peace. Over the years, world leaders have used sports as an instrument of foreign policy. They do that in both positive and negative ways. Sports are deployed positively when they are used to boost friendship or to support a noble cause and they are used negatively when they are deployed as an instrument of sanctions. This paper shows how sports were used as an instrument of foreign policy in Nigeria by three military regimes of Olusegun Obasanjo, Ibrahim Babangida and Sanni Abacha. Olusegun Obasanjo's regime pulled out Nigeria's Olympic contingents from participating in the 1976 Olympic Games in Montreal, Canada. Ten years later, the regime of Ibrahim Babangida led other Anglophone countries to boycott the 1986 Commonwealth Games in Edinburgh, Scotland; and in 1996 General Sanni Abacha stopped the Super Eagles from defending the title they won in the previous edition of the African Cup of Nations. Details of these boycotts and their political implications are discussed in this paper.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".