Are co‐linked business web sites really related? A link classification study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it