Librarian Authors Appear to Favour Open Access Journals, while Academic Authors Appear to Favour Non-Open Access Journals
Bibliographic record
Abstract
A Review of: Chang, Y.-W. (2017). Comparative study of characteristics of authors between open access and non-open access journals in library and information science. Library & Information Science Research, 39(1), 8-15. https://doi.org/10.1016/j.lisr.2017.01.002 Abstract Objective – To compare the characteristics of authors publishing in open access and non-open access library and information science (LIS) journals. Design – Comparative analysis of published journal articles. Setting – Academic journals. Subjects – Articles published in selected LIS journals between 2008-2013. Methods – Journals included in the Library Science and Information Science category in the 2012 edition of Journal Citation Reports and those listed in the Library and Information Science category of the Directory of Open Access Journals as of May 2013 were included in the analysis. Articles were examined and coded for author occupation, academic rank, and type of collaboration. Main Results – The author analyzed 1,807 articles from 20 open access journals and 1,665 articles from 13 non-open access journals. An unknown number of articles were excluded because they lacked required author information. Over half (53.9%) of the authors who published in the open access journals were practitioners. Over half (58.1%) of the authors who published in the non-open access journals were academics. Librarian-librarian collaboration was the most common type (38.6%) of collaboration found in the open access journals. Academic-academic collaboration was the most common type (34.1%) of collaboration found in the non-open access journals. Collaboration between librarians and academics was seen in 20.5% of open access articles and 13.2% of non-open access articles. Conclusion – In general, librarian-authored research was found more often in open access journals, while the “latest research topics and ideas” (p. 14) were found most often in non-open access journals.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.030 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".