Bibliographic record
Abstract
Purpose As the interest in evidence‐based librarianship increases, so does the need for a standardized practice methodology. One of the most essential components of EBL, critical appraisal, has not been fully established within the library literature. The purpose of this paper is to outline and describe a thorough critical appraisal tool and process that can be applied to library and information research in an evidence based setting. Design/methodology/approach To create a critical appraisal tool for EBL, it was essential to look at other models. Exhaustive searches were carried out in several databases. Numerous articles were retrieved which provided “evidence” or “best practice” based on a critical appraisal. The initial tool, when created, was distributed to several librarians who provided comments to the author regarding its exhaustiveness, ease of use and applicability and was subsequently revised to reflect their suggestions and comments. Findings The critical appraisal tool provides a thorough, generic list of questions that one would ask when attempting to determine the validity, applicability and appropriateness of a study. Originality/value More rigorous research and publishing will be encouraged as more librarians and information professionals adopt the practice of EBL and utilize this critical appraisal model
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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 | 0.377 | 0.656 |
| Meta-epidemiology (narrow) | 0.006 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.011 |
| Bibliometrics | 0.043 | 0.035 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.022 | 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".