Academic Librarians’ Conception and Use of Evidence Sources in Practice
Bibliographic record
Abstract
Objective – The objective of this study was to explore and understand how academic librarians use evidence in their professional decision making. The researcher aimed to gain insights on the relevance of the current EBLIP model to practice, and to understand the possible connections between scientific research and tacit knowledge within the practice of LIS. Methods – A grounded theory methodology was used, following the approach of Charmaz (2006). Participants were 19 academic librarians in Canada. Data was gathered via online diaries and semi-structured interviews over a six-month period in 2011. Results – Two broad types of evidence were identified (hard and soft), and are generally used in conjunction with one another. Librarians examine all evidence sources with a critical eye, and try to determine a complete picture before reaching a conclusion. As well, librarians use a variety of proactive and passive approaches to find evidence. Conclusions – These results provide a strong message that no single evidence source is perfect. Consequently, librarians bring different types of evidence together in order to be as informed as possible before making a decision. Using a combination of evidence sources, depending upon the problem, is the way academic librarians approach decision making.
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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 | 0.111 | 0.166 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.047 | 0.023 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.007 | 0.005 |
| 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".