Research in librarianship: issues to consider
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.028 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it