“Ask, Acquire, Appraise”: A Study of LIS Practitioners Participating in an EBLIP Continuing Education Course
Bibliographic record
Abstract
Objective – The project sought to examine the aspects of the question answering process in an evidence based library and information practice (EBLIP) context by presenting the questions asked, articles selected, and checklists used by an opportunistic sample of Australian and New Zealand library and information professionals from multiple library and information sectors participating in the “Evidence Based Library and Information Practice: Delivering Services That Shine” (EBLIP-Gloss) FOLIOz e-learning course. Methods – The researchers analyzed the “ask,” “acquire,” and “appraise” tasks completed by twenty-nine library and information professionals working in Australia or New Zealand. Questions were categorized by EBLIP domain, articles were examined to identify any comparisons, and checklists were collated by frequency. Results – Questions fell within each of the six EBLIP domains, with management being the most common. Timeliness, relevance, and accessibility were stronger determinants of article selection than rigour or study design. Relevance, domain, and applicability were the key determinants in selecting a checklist. Conclusion – This small-scale study exemplifies the EBLIP process for a self-selecting group of library and information professionals working in Australia and New Zealand. It provides a snapshot of the types of questions that library and information practitioners ask, and the types of articles and checklists found to be useful. Participants demonstrated a preference for literature and checklists originating from within the library and information science (LIS) field, reinforcing the imperative for LIS professionals to contribute to EBLIP research.
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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 | 0.053 | 0.109 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".