Making sense of liaison consultations: using reflection to understand information-seeking behavior
Bibliographic record
Abstract
Purpose – The purpose of this paper is to reflect on past liaison library practice to make sense of student information-seeking behavior. Experiential data created from liaison consultations was used to gain student perspectives. Liaison consultations enhance student experiences and provide strategic benefits for academic libraries to counter perceived under-utilization of information services in general. Design/methodology/approach – Grounded theory framed the study and thematic analysis was applied to liaison librarian consultation notes representing 25 years of past practice. Themes that characterize information-seeking behaviors were noted and used to help explain student behavior. Findings – Despite significant changes in information formats, the pervasive use of internet technology, and student searching habits, the key finding is that students will continue to consult with librarians to the extent that they find the experience useful. Both parties contribute and define the reference consultation to help formulate productive information-seeking behaviors. Originality/value – Analyzing evidence through the lens of reflection and the use of unobtrusive methods provided useful insights into the roles that librarians and students play in the consultation process. The findings suggest that information-searching behaviors can be influenced and shaped to produce successful searching outcomes. Several recommendations for strengthening library practice are provided.
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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 | 0.018 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".