Bibliographic record
Abstract
Academics are increasingly encouraged to use social media in their professional lives. Social networking sites are one type of tool within this; the ability to connect with others through this medium may offer benefits in terms of reaching novel audiences, enhancing research impact, discovering collaborators, and drawing on a wider network of expertise and knowledge. However, little research has focused on the role of these sites in practice, and their relationship to academics’ formal roles and institutions. This paper presents an analysis of 18 interviews carried out with academics in order to discuss their online networks (at either Academia.edu or ResearchGate, and Twitter) and to understand the relationship between their online networks and formal academic identity. Several strategies underpinning academics’ use of the sites were identified, including: circumventing institutional constraints, extending academic space, finding a niche, promotion and impact, and academic freedom. These themes also provide a bridge between academic identity development online and institutional roles, with different priorities for engaging with online networks being associated with different career stages.
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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 | 0.024 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.003 | 0.003 |
| 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".