Summer Meets Winter: African Nations Participating at the Winter Olympics, 1960–2018
Bibliographic record
Abstract
A record number of eight African countries competed at the 2018 Olympic Winter Games in PyeongChang, South Korea despite Africa being regarded as one of the hottest continents in the world. The continent’s almost complete lack of snow or ice weather conditions is the most obvious hurdle to African winter Olympic hopefuls. Although athletes from this continent compete successfully at the summer Olympics, very few African countries send teams of athletes to compete at the winter Olympics on a consistent basis. By 2014 less than a quarter of the fifty-four countries in Africa had ever competed at the winter Olympics, yet the history of African countries participating at the winter Olympics dates back almost six decades. The first appearance of an African country at the winter Olympics was at the 1960 games in Squaw Valley, USA, when South Africa participated for the first time. Since 1984, at least one African nation has competed at each subsequent winter Olympics. The lack of climate for winter sports, such as bobsleigh, skiing and snowboarding, limits the level of participation in winter sports. However, globalization and the relatively limited access to tertiary institutions in Africa have brought young African athletes in contact with many forms of winter sport while studying or working abroad, predominantly in the northern hemisphere.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".