Wayfinding Research in Library and Information Studies: State of the Field
Bibliographic record
Abstract
Abstract Objective – Often people enter libraries focused on their primary information needs and haven't considered their need for spatial information to find their way to what they need. This presents unique wayfinding information challenges for libraries. Papers on library wayfinding often include some discussion of the lack of wayfinding research in libraries, but apparently there has been no comprehensive review of the LIS literature on wayfinding. Methods – This paper is a comprehensive review of library wayfinding literature, using the Library, Information Science & Technology Abstracts with Full Text (via EBSCOhost) database to collect the dataset. Results – Findings indicate a small collection of library wayfinding research, primarily focused on academic libraries. Conclusion – Empirical research in this area is limited. Suggestions for future research on library wayfinding, including potential foci for that research, are presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.066 | 0.146 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.023 | 0.050 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.030 | 0.019 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".