Networked Scholarship and Motivations for Social Media use in Scholarly Communication
Bibliographic record
Abstract
Research on scholars’ use of social media suggests that these sites are increasingly being used to enhance scholarly communication by strengthening relationships, facilitating collaboration among peers, publishing and sharing research products, and discussing research topics in open and public formats. However, very few studies have investigated perceptions and attitudes towards social media use for scholarly communication of large cohorts of scholars at national level. This study investigates the reasons for using social media sites for scholarly communication among a large sample of Italian university scholars (N=6139) with the aim of analysing what factors mainly affect these attitudes. The motivations for using social media were analysed in connection with frequency of use and factors like gender, age, years of teaching, academic title, and disciplinary field. The results point out that for the most used tools the influence of the variables examined was higher in shaping scholars’ motivations. In fact, frequency of use, age, years of teaching, and disciplinary field were found to be relevant factors especially for LinkedIn and ResearchGate-Academia.edu, while gender and academic title seemed to have a limited impact on scholars’ motivations for all social media sites considered in the study. Considerations for future research are provided along with limitations of the study.
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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".