Comparative research of web technology application of Olympic Committee official websites of China,U.S.A,Australia and Canada
Bibliographic record
Abstract
A comparative analysis of the web technology application of official websites of the National Olympic Committee of China,U.S.A,Australia and Canada was conducted into web security,search engine,site map,database,web developing language,navigation system,labeling of navigation elements and multilanguage versions.The research showed that,while the official website of the Chinese Olympic Committeeled in the aspects of providing multi-language versions,and facilitating English-speaking users' direct access to its English version,there were areas for further improvement.It was suggested that,in order to meet the needs of the forthcoming Beijing Olympic Games in 2008 and distinguish itself among the sports websites of the world,the official website of the Chinese Olympic Committee should strengthen awareness of web security,mistake-correction and index-reminding function of search system and close PING function.It also should use site map function,improve configurable database function,choose idiomatic English usages as labeling words,increase in-links within the website,and synchronize contents in different language versions.
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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".