Employing Evidence: Does it Have a Job in Vocational Libraries?
Bibliographic record
Abstract
Objective - Evidence based librarianship (EBL) springs from medical and academic origins. As librarians are tertiary educated (only occasionally with supplementary qualifications covering research and statistics) EBL has had an academic focus. The EBL literature has significant content from school and university perspectives, but has had little, if any, vocational content. This paper suggests a possible Evidence Based Librarianship context for vocational libraries. Methods - A multidisciplinary scan of evidence based literature was undertaken, covering medicine and allied health, librarianship, law, science and education. National and international vocational education developments were examined. The concept and use of evidence in vocational libraries was considered. Results - Library practice can generally benefit from generic empirical science methodologies used elsewhere. Different areas, however, may have different concepts of what constitutes evidence and appropriate methodologies. Libraries also need to reflect the evidence used in their host organisations. The Australian vocational librarian has been functioning in an evidence based educational sector: national, transportable, prescriptive, competency based and outcome driven Training Packages. These require a qualitatively different concept of evidence compared to other educational sectors as they reflect pragmatic, economic, employability outcomes. Conclusions - Vocational and other librarians have been doing research but need to be more systematic about design and analysis. Librarians need to develop ‘evidence literacy’ as one of their professional evaluation skills. Libraries will need to utilise evidence relevant to their host organisations to establish and maintain credibility, and in the vocational sector this is set in a competency based framework. Competency based measures are becoming increasingly relevant in school and university (including medical) education.
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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 | 0.193 | 0.391 |
| Meta-epidemiology (narrow) | 0.000 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.013 | 0.040 |
| Scholarly communication | 0.048 | 0.048 |
| Open science | 0.007 | 0.024 |
| Research integrity | 0.021 | 0.014 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".