Why are Hyperlinks to Business Websites Created? A Content Analysis
Bibliographic record
Abstract
Motivations for the creation of hyperlinks to business sites were analyzed through a content analysis approach. Most links were created for business purposes confirming findings from early quantitative studies that links contain useful business data. Links to competitors were extremely rare but competitors were often co-linked, suggesting that co-link analysis is the direction to pursue for information on competitive intelligence.Les motivations pour la création d’hyperliens pour les sites commerciaux ont été analysées à l’aide de l’approche de l’analyse de contenu. La plupart des liens ont été créés dans l’objectif d’une utilisation commerciale, confirmant les résultats obtenus des études quantitatives préliminaires suggérant que ces liens contiennent des données commerciales utiles. Les liens vers la concurrence étaient extrêmement rares, cependant les concurrents étaient souvent co-liés, suggérant que l’analyse des co-liens est la voie à poursuivre en ce qui concerne l’information en veille concurrentielle.
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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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".