Social Media and Libraries:A Scientometric Assessment of World Output, 2003-2014
Bibliographic record
Abstract
The paper examines 1472 global publications on "social media and libraries" covering the period 2003-14 on a series of indicators. The publications output averaged 62% annual growth. These 1472 global publications received 5350 citations since their publication, averaging 1.68 citations per paper. Only 47.55% publications were cited one or more times. The contribution of top most productive countries (namely USA, U.K., Canada, Spain, Germany, China, Australia, India, Netherlands and Singapore) together accounted for 74.86% share. Netherlands registered the highest share (38.89%) of international collaborative papers among the top 10 countries during 2003-14. The top 15 organizations out of 293 accounted for 12.57% share. The top 10 authors out of 410 accounted for 4.96% share during 2003-14. Journals (57%) and conference proceedings (26.09%) contributed the largest share to global output during 2003-14. The top 15 journals contributed 290 publications(34.20%) during 2003-14. The top 10 most highly cited papers received 1094 citations, from 51 to 401 citations per paper. Blogs contributed the largest share (34.99%) of publications among social media sites, followed by Wikipedia (19.97%), Facebook (13.65%) and others.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.091 | 0.157 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".