Variations in Use of Meta Tag Keywords by Web Pages in Different Languages
Why this work is in the frame
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Bibliographic record
Abstract
Sets of top-ranking pages in 19 languages returned by the Google search engine were downloaded and their titles and meta tagged keywords analyzed. Results showed significant differences in proportion of pages with keywords depending on language; specifically, pages in Dutch, French, and German showed the highest proportions with keywords, while pages in Chinese and Korean showed the lowest proportions. Keywords were mostly in the languages of the pages, though on Chinese, Greek, Indonesian, and Turkish pages keywords in English or in English mixed with other languages predominated. The proportion of very long titles also varied significantly with language, with nearly 10% of titles on Russian pages exceeding 100 bytes, in contrast to less than 1% on Chinese, Finnish, Indonesian, and Polish pages. Both standard ASCII extensions and character entity references were used to code special characters in titles.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.016 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it