Resources and Capabilities from Their Very Outset: a Bibliometric Comparison Between Scopus and the Web of Science
Bibliographic record
Abstract
The bibliometric method has proven to be a powerful tool for the analysis of scientific publications, in such a way that allows rating the quality of the knowledge generating process, as well as its impact on firm´s environment. This article presents a comparison between two powerful bibliographic databases in terms of their coverage and the usefulness of their content. The comparison starts with a subject associated to the relationship between resources and capabilities. The outcomes show that the search results differ between both databases. The Web Of Science (WOS), has a greater coverage than SCOPUS has. It also has a greater impact in terms of most cited authors and publications. The search results in the WOS yield articles from 2001, while Scopus yields articles from 1976, however, some of the latter are inconsistent with the topic being searched. The analysis points to a lack of studies regarding resources as foundations of firm´s capabilities; as a result, new research on this field is suggested.
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 | 0.006 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.071 | 0.104 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".