Patterns of web linking to heterogeneous groups of companies
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 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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