Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the robustness of web co‐link analysis for business intelligence. Design/methodology/approach The method is tested in two different Chinese industries, the electronics/IT industry and the chemical industry. Web co‐link data are collected in two different time periods from a different search engine in each period. Multidimensional scaling (MDS) is used to map the co‐link data into business competition positions. Findings Web co‐link analysis is fairly robust in that the mapping results reflect fairly well the business competition landscape for both industries and in both time periods. The mapping results are better when the data collection is restricted to Chinese language webpages only. The study also finds that the Chinese webpages are very consumer‐oriented, a phenomenon that is not seen in previous studies of international companies. Originality/value This paper contributes to the understanding of the robustness and applicability of the co‐link analysis method. The method is useful for business intelligence and can also be applied to the non‐business environment. The paper also contributes to the understanding of a specific Chinese web phenomenon.
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.
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 | 0.081 | 0.473 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".