The Realities of Relevance: A Survey of Librarians' Use of Library and Information Science Research
Bibliographic record
Abstract
This article grew out the authors' desire to explore the widely held notion that librarians disregard LIS research because they consider it irrelevant. For example, in the early stages of this project one colleague commented that librarianship "is all practice" and that LIS research has had no effect upon his own work. Editors of many LIS journals also question whether research exerts influence on practice. Peter Hernon and Candy Schwartz, editors of Library and Information Science Research, lament that “research has not penetrated the soul” of the library profession, and William Katz, former editor of Research Quarterly, notes that many authors have failed to show the implications of their research for practice. A survey of LIS scholars revealed that many researchers themselves doubt whether their findings affect practice. While many authors within the profession have thus agreed upon the existence of a research-practice gap in librarianship, they differ in regards to the gap's causes. Some authors blame researchers; some blame practitioners; and some attribute the breakdown to deficiencies in LIS education or dissemination channels. This article examines the research-practice gap by discussing the results of a recent survey that measured the use of LIS research among Alabama’s academic reference librarians.
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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 | 0.026 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".