A webometric analysis of major keywords and expressions in biochemistry using LexiURL Searcher
Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore webometric analysis of keywords and expressions of the biochemistry field of study via LexiURL Searcher. Design/methodology/approach – Interfaces for assisting users with information access have received considerable attention. Along with the extraction of data on Web sites for webometric purposes (e.g. link analysis, ranking of Web sites, etc.), LexiURL Searcher presents some information on the arrangement of links among different Web sites. Such capability enables users to identify one or more Web sites around their intended subject and, accordingly, explore all Web sites linked with their identified Web site(s). LexiURL Searcher has preceded webometric analysis by considering the main expressions and keywords derived from the MeSH database. Findings – The worldwide survey indicated that links from countries such as England, Japan, Germany, Australia and Canada were among the Web sites that are most used in biochemistry. Alternatively, other countries such as Singapore, Thailand and Poland had the most advantageous links to the outside world, whereas South Africa, New Zealand and The Netherlands had the least link effect. Biochemistry, being a specialized domain, would benefit greatly from site linking and would provide users the most assistance in information processing. Originality/value – Most webometric studies remain on the level of link analysis and Web site statuses; however, this paper gives information on the common thread Web sites based on a standard thesaurus.
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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 | 0.007 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.062 | 0.078 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".