Building visualization skills through investigating the Journal of the Medical Library Association coauthorship network from 2006–2017
Bibliographic record
Abstract
Objective: The primary objective of this study was to explore different dimensions of Journal of the Medical Library Association (JMLA) authorship from 2006–2017. Dimensions that were evaluated using coauthorship networks and affiliation data included collaboration, geographical reach, and relationship between Medical Library Association (MLA) member and nonmember authors. A secondary objective was to analyze the practice and practical application of data science skills.Methods: A team of librarians who attended the 2017 Data Science and Visualization Institute used JMLA bibliographic metadata extracted from Scopus, together with select MLA membership data from 2006–2017. Data cleaning, anonymization, analysis, and visualization were done collaboratively by the team members to meet their learning objectives and to produce insights about the nature of collaborative authorship at JMLA.Results: Sixty-nine percent of the 1,351 JMLA authors from 2006–2017 were not MLA members. MLA members were more productive and collaborative, and tended to author articles together. The majority of the authoring institutions in JMLA are based in the United States. Global reach outside of the United States and Canada shows higher authorship in English-speaking countries (e.g., Australia, United Kingdom), as well as in Western Europe and Japan.Conclusions: MLA support of JMLA may benefit a wider network of health information specialists and medical professionals than is reflected in MLA membership. Conducting coauthorship network analyses can create opportunities for health sciences librarians to practice applying emerging data science and data visualization skills.
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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 | 0.023 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".