Bibliographic record
Abstract
Purpose Attempting to incorporate research into decision making raises several questions about the research that currently exists in librarianship, areas that are most in need of research, obstacles to conducting research, and possible solutions for nurturing a professional environment in which conducting and using research becomes an accepted and expected part of our practice. This article attempts to answer some of those questions. Design/methodology/approach A general overview of the research base in librarianship is given. Compilation of content analyses and systematic reviews present an argument relating to the need of further research in librarianship. Further examination of potential research questions is conducted, and potential obstacles and solutions to research barriers are presented. Findings There is still a need to establish a solid evidence base within our profession. With support from all sectors of librarianship, progress can be made. Originality/value This paper points out gaps in our research knowledge, and areas that need to be explored via research in library and information studies. It is hoped that this paper will encourage librarians to think about how they can incorporate research into their daily practice.
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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 | 0.418 | 0.511 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.008 | 0.018 |
| Science and technology studies | 0.022 | 0.081 |
| Scholarly communication | 0.057 | 0.081 |
| Open science | 0.010 | 0.022 |
| Research integrity | 0.058 | 0.038 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".