School Librarianship and Evidence Based Practice: Progress, Perspectives, and Challenges
Bibliographic record
Abstract
Objective – This paper provides an overview of progress and developments surrounding evidence based practice in school librarianship, and seeks to provide a picture of current thinking about evidence based practice as it relates to the field. It addresses current issues and challenges facing the adoption of evidence based practice in school librarianship. Methods – The paper is based on a narrative review of a small but growing body of literature on evidence based practice in school librarianship, set within a broader perspective of evidence based education. In addition, it presents the outcomes of a collaborative process of input from 200 school libraries leaders collected at a School Library summit in 2007 specifically to address the emerging arena of evidence based practice in this field. Results – A holistic model of evidence based practice for school libraries is presented, centering on three integrated dimensions of evidence: evidence for practice, evidence in practice, and evidence of practice. Conclusion – The paper identifies key challenges ahead if evidence based school librarianship is to develop further. These include: building research credibility within the broader educational environment; the need for ongoing review and evaluation of the diverse body of research in education, librarianship and allied fields to make quality evidence available in ways that can enable practicing school librarians to build a culture of evidence based practice; development of tools, strategies, and exemplars to use to facilitate evidence based decision-making; and, ensuring that the many and diverse advances in education and librarianship become part of the practice of school librarianship.
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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 | 0.136 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.045 | 0.038 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".