Use of ResearchGate and Academia.edu by Austrian Professors from Business Administration
Bibliographic record
Abstract
The aim of this article is to investigate the presence of Austrian university professors from the field of business administration ( n = 233) in ResearchGate, Academia.edu , and, for reasons of comparison, Scopus and identify possible correlations between the indicators offered by these sites. The results reveal that 95 per cent of the researchers considered are represented on Scopus and that 78 per cent have an account on ResearchGate, while Academia.edu is used by only 42 per cent. As the bibliometric and social indicators related to ResearchGate show, professors actively use this site to promote and share their research with other colleagues. This is different for Academia.edu , which is only regarded as a ‘second choice’ by many researchers. The correlations of the Scopus metrics with the bibliometric metrics on ResearchGate are very strong, while those with the usage metrics are ‘only’ strong (between 0.7 and 0.76).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.017 | 0.138 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".