Bibliographic record
Abstract
This is 29th Olympic Games since the 1st Olympic Game was held in Athens in 1896.\nChina, the host country and total 11 teams, Japan, North Korea, Nigeria, U.S.A., Canada,\nArgentina, Brazil, New Zealand, German, Norway, and Sweden that got through a qualifying\nleague in each continental took a spot for this Olympic Games. These 12 teams were divided\ninto 3 groups, 4 teams for each, and played for a qualifying league. Total 8 teams, top 2 teams\nin each league and top 2 teams in 3rd team of each league went to the final round.\nJapan women’s national team“Nadeshiko Japan”won the fourth prize in Beijing\nOlympic, which was a remarkable progress in the history of Japan women’s soccer. This big\nstep exceedingly contributed to women’s soccer in Japan being popular.\nThis paper focused on 4 factors,“Analysis”,“Goal setting”,“Preparation”, and\n“Preparedness”, to explore why“Nadeshiko Japan”took 4th place in Beijing Olympic.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.118 | 0.056 |
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".