Developing evidence‐based librarianship: practical steps for implementation*
Bibliographic record
Abstract
Evidence-based librarianship (EBL) is a relatively new concept for librarians. This paper lays out a practical framework for the implementation of EBL. A new way of thinking about research in librarianship is introduced using the well-built question process and the assignment of librarian research questions to one of six domains specific to librarianship. As a profession, librarianship tends to reflect more qualitative, social sciences/humanities in its research methods and study types which tend to be less rigorous and more prone to bias. Randomised controlled trials (RCT) do not have to be placed at the top of an evidence 'hierarchy' for librarianship. Instead, a more encompassing model reflecting librarianship as a whole and the kind of research likely to be done by librarians is proposed. 'Evidence' from a number of disciplines including health sciences, business and education can be utilized by librarians and applied to their practice. However, access to and availability of librarianship literature needs to be further studied. While using other disciplines (e.g. EBHC) as a model for EBL has been explored in the literature, the authors develop models unique to librarianship. While research has always been a minor focus in the profession, moving research into practice is becoming more important and librarians need to consider the issues surrounding research in order to move EBL forward.
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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 | 0.381 | 0.317 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.029 | 0.034 |
| Open science | 0.013 | 0.041 |
| Research integrity | 0.022 | 0.029 |
| Insufficient payload (model declined to judge) | 0.020 | 0.008 |
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".