To be published in Papers of Europe, special issue, Autumn 2012: IS HOSTING THE GAMES ENOUGH TO WIN? A PREDICTIVE ECONOMIC MODEL OF MEDAL WINS AT 2014 WINTER OLYMPICS
Bibliographic record
Abstract
Starting from an econometric model which performed well in explaining medal wins in the previous Summer Olympics and predicting the medal distribution across nations at the Beijing Olympics, an adapted model is built up to achieve the same job for the Winter Games. When estimating the model for Winter Olympics the variables that determine the distribution of medal wins are: the level of economic development (GDP per inhabitant), population, a host country advantage, the political regime, the number of winter sports resorts and, to a lesser extent, the annual level of snow coverage. With this estimated model, the prediction of medal wins at the 2014 Sochi Games forecasts the biggest number of medals to be won by the U.S. team followed by Germany, Canada and Russia. Of course, such prediction may always reveal to be partly wrong due to possible sporting surprises which might emerge during the actual 2014 Games. Soon after Russia has got the organisation of Winter Games in Sochi in 2014, official expectation, namely mentioned by President Vladimir Putin, has become that Russia will win the Games she is going to host. A same feeling has been reinforced by a high Olympic performance of China at Beijing Summer Games in 2008 since the host country has won the greatest number of gold medals and the second overall number of medals, compared to all 1
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".