A critical appraisal tool for library and information research
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 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
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.013 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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