Personal Publications Lists Serve as a Reliable Calibration Parameter to Compare Coverage in Academic Citation Databases with Scientific Social Media
Bibliographic record
Abstract
A Review of: Hilbert, F., Barth, J., Gremm, J., Gros, D., Haiter, J., Henkel, M., Reinhardt, W., & Stock, W.G. (2015). Coverage of academic citation databases compared with coverage of scientific social media: personal publication lists as calibration parameters. Online Information Review 39(2): 255-264. http://dx.doi.org/10.1108/OIR-07-2014-0159 Abstract Objective – The purpose of this study was to explore coverage rates of information science publications in academic citation databases and scientific social media using a new method of personal publication lists as a calibration parameter. The research questions were: How many publications are covered in different databases, which has the best coverage, and what institutions are represented and how does the language of the publication play a role? Design – Bibliometric analysis. Setting – Academic citation databases (Web of Science, Scopus, Google Scholar) and scientific social media (Mendeley, CiteULike, Bibsonomy). Subjects – 1,017 library and information science publications produced by 76 information scientists at 5 German-speaking universities in Germany and Austria. Methods – Only documents which were published between 1 January 2003 and 31 December 2012 were included. In that time the 76 information scientists had produced 1,017 documents. The information scientists confirmed that their publication lists were complete and these served as the calibration parameter for the study. The citations from the publication lists were searched in three academic databases: Google Scholar, Web of Science (WoS), and Scopus; as well as three social media citation sites: Mendeley, CiteULike, and BibSonomy and the results were compared. The publications were searched for by author name and words from the title. Main results – None of the databases investigated had 100% coverage. In the academic databases, Google Scholar had the highest amount of coverage with an average of 63%, Scopus an average of 31%, and lowest was WoS with an average of 15%. On social media sites, Bibsonomy had the highest coverage with an average of 24%, Mendeley had an average coverage of 19%, and the lowest coverage was CiteULike with an average of 8%. Conclusion – The use of personal publication lists are reliable calibration parameters to compare coverage of information scientists in academic citation databases with scientific social media. Academic citation databases had a higher coverage of publications, in particular, Google Scholar, compared to scientific social media sites. The authors recommend that information scientists personally publish work on social media citation databases to increase exposure. Formulating a publication strategy may be useful to identify journals with the most exposure in academic citation databases. Individuals should be encouraged to keep personal publication lists and these can be used as calibration parameters as a measure of coverage in the future.
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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 | 0.088 | 0.411 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.038 | 0.064 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".