Mendeley: teaching scholarly communication and collaboration through social networking
Bibliographic record
Abstract
Purpose This paper aims to highlight the productivity and collaborative features of Mendeley, a reference management tool, as well as recommendations on how Mendeley can be incorporated into an information literacy program. Design/methodology/approach Results from a literature review and feedback from students and faculty were used to provide background for this paper. Mendeley's features and potential benefits to librarians and researchers are discussed. Findings Feedback from students and faculty who use Mendeley are very positive owing to its productivity and social networking and collaboration features. The literature highlights Mendeley's usefulness in the context of citation management software. Practical implications The paper provides useful tips and best practices for integrating Mendeley into information literacy sessions and workshops for students and faculty. The paper also discusses how teaching Mendeley can facilitate scholarly communication between researchers and broaden the role of librarians on campus. Originality/value The paper shows that Mendeley enables higher level information literacy by helping users focus on locating and organizing information and spend less time on citation details. Mendeley's social networking features are compatible with emerging work practices, facilitating collaboration among researchers through group's functions and open sharing of information through groups and publication lists.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".