Equal Representation by Search Engines? A Comparison of Websites across Countries and Domains
Bibliographic record
Abstract
The study examined search engine coverage of websites across countries and domains. Websites in four domains (commercial, educational, governmental, and organizational) from four countries (U.S., China, Singapore, and Taiwan) were randomly sampled by custom-built computer programs and then manually filtered for their suitability for the study. Representation of the 1,664 sampled sites in four major search engines (Google, Yahoo!, MSN, and Yahoo! China) was examined in terms of whether the site was covered and the number of pages indexed by the search engines. The study found that U.S. sites received higher coverage rates than their counterparts in other countries. The language of a site did not affect the site’s chance of being indexed by search engines. Sites that were more visible had a higher chance of being indexed, but this factor did not seem to explain the differentiated coverage across countries. Yahoo! China provided better coverage of sites from China and surrounding regions than its global counterpart, Yahoo!. The poor coverage of Chinese commercial and governmental sites is noted and the implications are discussed in light of the tremendous development of the Web in China.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".