Are co‐linked business web sites really related? A link classification study
Bibliographic record
Abstract
Purpose The purpose of this article is to examine the reasons for the creation of co‐links between pairs of business web sites. Specifically, to determine whether co‐linked business web sites are really related. Design/methodology/approach Co‐links to 32 telecommunications companies were retrieved using Yahoo! and a random sample of 495 co‐linking pages (the page that initiated the co‐link) were selected for a content analysis. The context of the co‐link and the content of the co‐linking page were manually examined to record the following data: type of web site and the reason for the creation of the co‐link. Findings The study found that 61.4 per cent of co‐links were created to connected pairs of highly related businesses (related companies, related products, and related services). Only 14.7 per cent of co‐links were created for non‐business reasons. The remaining 23.8 per cent of co‐linked sites showed a loose or marginal business relationship. The study also found that co‐links targeting home pages (as opposed to non‐homepages) were more likely to connect related businesses. Furthermore, co‐links coming from commercial sites (as opposed to other sites such as educational sites) are more likely to link related businesses. Originality/value The findings from this content analysis study confirm results from previous quantitative studies that showed that web co‐links measure relatedness of co‐linked sites and that co‐links can be objects of web data mining. The study contributes to our understanding of link motivations and the web linking phenomenon in general. The difference between links to homepages and that to non‐homepages found in the study can guide us in co‐link data collection.
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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 | 0.010 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.014 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".