Extending Web Co-link Analysis to Web Co-word Analysis for Competitive Intelligence
Bibliographic record
Abstract
Abstract: The study carried out Web co-link analysis and Web co-word analysis for a group of companies in the international shipping industry. Co-link and co-word data were collected and analyzed with MDS. Results from different data sets were compared and advantages and disadvantages of the two methods were examined. Résumé: Cette étude a effectué une analyse des co-liens et des cooccurrence de termes sur le web pour un groupe de sociétés de l'industrie du transport international. Les données ont été recueillies et analysées par échelonnement multidimensionnel. Les résultats provenant de différents jeux de données ont été comparées et les avantages et désavantages des deux méthodes, examinées. 1. Background of the study In the area of Web data mining for competitive intelligence, studies have found that the number of links pointing to a company’s Website (inlinks) correlates significantly with the company’s business performance measures such as revenue and profit (Vaughan, 2004). The correlation is significant even after factors such as the size of the company is accounted for. This demonstrates that inlinks contain useful business information.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".