Bibliographic record
Abstract
Purpose To construct web visibility profiles of news web sites by examining hyperlinks pointing to the sites. Design/methodology/approach National newspapers from USA (USA Today), Canada (The Globe and Mail), China (People's Daily) as well as Hong Kong (Sing Tao Daily) were selected for the study. A total of 1,859 links pointing to the four news sites were manually classified into the four aspects of language, country, types of sites, and reasons or purposes for linking. Findings A comparison of the four news sites provided useful information on their web visibility. The Globe and Mail seemed to have a larger international reach than USA Today. Neither newspaper web site attracted links from China or from pages in the Chinese language. Outside China, People's Daily, an official Chinese Government newspaper, is not as visible as Hong Kong based Sing Tao Daily. USA Today and The Globe and Mail were used more for news citing or reprinting purposes while People's Daily seemed to be used more as a research resource. Research limitations/implications Link analysis like this provides us with only an indirect view of the online readership and the methodology has limitations. Not all readers create links to the newspaper sites that they visit. Readers could be led to a news site through other venues including “social bookmarking” services. Practical implications The study shows that link analysis is a novel and useful method that journalists and information professionals can use to gauge online readership and potential impact of news sites. Originality/value Presented a novel method that complements but not replaces other web user studies such as web server log analysis.
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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".