Library and Information Science Research Literature is Chiefly Descriptive and Relies Heavily on Survey and Content Analysis Methods
Bibliographic record
Abstract
A Review of: Aytac, S. & Slutsky, B. (2014). Published librarian research, 2008 through 2012: Analyses and perspectives. Collaborative Librarianship, 6(4), 147-159. Objective – To compare the research articles produced by library and information science (LIS) practitioners, LIS academics, and collaborations between practitioners and academics. Design – Content analysis. Setting – English-language LIS literature from 2008 through 2012. Subjects – Research articles published in 13 library and information science journals. Methods – Using a purposive sample of 769 articles from selected journals, the authors used content analysis to characterize the mix of authorship models, author status (practitioner, academic, or student), topic, research approach and methods, and data analysis techniques used by LIS practitioners and academics. Main Results – The authors screened 1,778 articles, 769 (43%) of which were determined to be research articles. Of these, 438 (57%) were written solely by practitioners, 110 (14%) collaboratively by practitioners and academics, 205 (27%) solely by academics, and 16 (2%) by others. The majority of the articles were descriptive (74%) and gathered quantitative data (69%). The range of topics was more varied; the most popular topics were libraries and librarianship (19%), library users/information seeking (13%), medical information/research (13%), and reference services (12%). Pearson’s chi-squared tests detected significant differences in research and statistical approaches by authorship groups. Conclusion – Further examination of practitioner research is a worthwhile effort as is establishing new funding to support practitioner and academic collaborations. The use of purposive sampling limits the generalizability of the results, particularly to international and non-English LIS literature. Future studies could explore motivators for practitioner-academic collaborations as well as the skills necessary for successful collaboration. Additional support for practitioner research could include mentorship for early career librarians to facilitate more rapid maturation of collaborative research skills and increase the methodological quality of published research.
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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 | 0.134 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.056 | 0.072 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.014 |
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".