Evidence-Based Practice and Qualitative Research: A Primer for Library and Information Professionals
Bibliographic record
Abstract
Objective - This paper discusses the importance of qualitative research in evidence-based library and information practice (EBLIP), with a focus on practical tips for evaluating and implementing effective qualitative research projects. Methods - The paper provides a brief introduction to the nature of qualitative inquiry and its status within current models of evidence assessment. Three problems of excluding qualitative research from the evidence-base in library and information studies (LIS) are identified: 1) ignoring the social sciences and humanities traditions that inform research in the field; 2) privileging of quantitative and experimental methods over others in evidence assessment; and, 3) focusing attention away from the best evidence for LIS research problems. Results - Qualitative approaches commonly used in library and information contexts are discussed, along with strategies for assessing quality in this work and some of the common ethics-related issues that researchers and professionals must consider. Conclusions - LIS professionals are encouraged to: 1) select research methods – including qualitative approaches – that best suit LIS questions; 2) design collaborative projects that combine quantitative and qualitative approaches, that will address research questions in a more complete way; 3) consider qualitative measures of rigor in assessing quality – rather than imposing quantitative expectations; and 4) revise existing models of “evidence” to recognize the value and rigor of qualitative research projects.
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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 | 0.229 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.007 | 0.038 |
| Scholarly communication | 0.019 | 0.026 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".