Book Review: Being Evidence Based in Library and Information Practice, edited by Denise Koufogiannakis and Alison Brettle
Bibliographic record
Abstract
Being Evidence Based in Library and Information Practice builds on earlier approaches to evidence-based librarianship with an expanded model that values and incorporates local evidence and professional knowledge alongside research evidence.Editors Denise Koufogiannakis and Alison Brettle were involved in the introduction of EBLIP (evidence-based library and information practice) to librarianship and LIS research as well as in the establishment of the journal Evidence Based Library and Information Practice, and they bring their experience and refection on the history of this movement to this new model.The model acknowledges the importance of context and experience to decision-making in libraries:This process puts the practitioner . . . in the centre of the process. . . .It incorporates the use of best evidence, whatever that may be, depending on the situation.It enables librarians to practice in an informed and thoughtful manner, bringing together the art and science of the profession.( 17)
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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 | 0.015 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".