Stakeholder management: a bibliometric analysis to understand the evolution of the research field
Bibliographic record
Abstract
Objetive. The aim of this paper is to provide a general overview of research performed in stakeholder management using bibliometric methods, to analyze three main relevant factors: general productivity, research approaches and influence structure at country, institution, author and related subject´s level.Method. The analysis made in the present work will take into consideration bibliometric indicators.Results. The main advantage of this approach is that it identifies the most productive and influential authors, journals, institutions and countries are presenting the major productivity in the field. By doing so, the reader can clearly identify where is the leading research taking place since 1969 to the date. In what corresponds to the research questions, the main findings are listed as follows.Conclusions. the results show that there is an important concentration of productivity mainly in seven countries: United States, United Kingdom, Australia, Canada, Netherlands, Germany and Spain, with an overall predominance of the United States in terms of total citation.
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How this classification was reachedexpand
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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.139 | 0.397 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".