Patterns of web linking to heterogeneous groups of companies
Bibliographic record
Abstract
Purpose The paper seeks to extend co‐link analysis to web sites of heterogeneous companies belonging to different industries and countries, and to cluster companies by industries and compare results from different countries. Design/methodology/approach Web sites of 255 companies that belong to five stock exchange indexes were included in the study. Data on co‐links pointing to these web sites were gathered using Yahoo!. Co‐link data were analyzed using multidimensional scaling (MDS) to generate MDS maps that would position companies based on their co‐link counts. Findings Comparisons of results across different countries and economies showed the following overall pattern: companies whose businesses are information‐based tend to form well‐defined clusters, while companies operating on a more traditional business model tend not to form clear groups. A comparison between the EU zone and the USA suggests that the EU economy is not well integrated yet. Practical implications The findings from the study suggest the possibility of using co‐link analysis to distinguish information‐based industries from traditional industries. Originality/value The paper extends co‐link analysis from a single industry to heterogeneous industries with global and complex business phenomena.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| 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".