Web co‐word analysis for business intelligence in the Chinese environment
Bibliographic record
Abstract
Purpose The study seeks to apply Web co‐word analysis to the Chinese business environment to test the feasibility of the method there. Design/methodology/approach The authors selected a group of companies in two Chinese industries, collected co‐word data for the companies, analyzed the data with multidimensional scaling (MDS), and then compared the MDS maps generated from the co‐word data with business situations to find out if the co‐word method works. Findings The study found that the Web co‐word method could potentially be applied to the Chinese environment. The study also found the advantages and disadvantages of the Web co‐word method vs the Web co‐link method. Originality/value Knowing the applicability of the Web co‐word method to the Chinese environment contributes to the knowledge of this new Webometrics method. Mining business information from the Web is more valuable when applied to a foreign country where language and culture barriers exist. To use the co‐word method, one does not have to be able to read or write in that language. One only needs to have the names of the companies to study, which can be easily obtained without knowledge of the language. The value of business information about countries such as China is obvious given the global nature of contemporary business competition and the significance of the Chinese economy to the world.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".