Social Networking Tools for Informal Scholarly Communication Prove Popular for Academics at Two Universities
Bibliographic record
Abstract
Objective – To investigate the adoption, use, perceived impact of, and barriers to using social networking tools for scholarly communication at two universities. Design – Cross-institutional quantitative study using an online survey. Setting – Academics working in the disciplines of the humanities and social sciences at two universities: one in Europe and one in the Middle East. Methods – An online survey was devised based on a previous survey (Al-Aufi, 2007) and informed by relevant research. The survey was piloted by 10 academics at the 2 participating universities. Post pilot it was revised and then circulated to all academics from similar faculties at two universities. Three follow up emails were sent to both sets of academics. The data was analyzed using Statistical Package for the Social Sciences (SPSS) software. Descriptive and inferential statistics were analyzed using ANOVA tests. Main Results – The survey achieved a 34% response rate (n=130). The majority of participants were from the university based in the Middle East and were male (70.8%). Most of the responses were from academics under 40 years of age. The use of notebooks was prevalent at both universities. “Notebooks” is used as a term to describe laptops, netbooks, or ultra-book computers. The majority reported use of social networking tools for informal scholarly communication (70.1%), valuing this type of use. 29.9% of respondents reported they do not use social networking tools for this purpose. Barriers were identified as lack of incentive, digital literacy, training, and concerns over Internet security. Among the non-users, barriers included low interest in their use and a perceived lack of relevancy of such tools for scholarly communication. The types of tools used the most were those with social connection functions, such as Facebook and Twitter. The tools used the least were social bookmarking tools. A one-way analysis of variance (ANOVA) test indicated that there was no significant difference at the 0.05 level between the use of social networking tools at both universities, with the exception of using tools to communicate with researchers locally and with publishers at one of the universities. Both universities use tools for communication with peers and academics internationally. The responses were mainly positive towards the perceived usefulness of social networking tools for informal scholarly communication. Conclusion – The authors conclude that despite the small sample of the community of academics investigated, there is a general trend towards increasing use and popularity of social networking tools amongst academics in the humanities and social sciences disciplines. As technology advances, the use of such tools is likely to increase and advance among academics. The authors point to pathways for future research including expanding the methods to include interviews, focus groups, and case studies. Another angle for research of interest is interdisciplinary differences in the use of prevalent tools such as Facebook and Twitter.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".