Library Users Attempt to Access a Wide Range of Information Beyond Books and Articles through a Single Search Box
Bibliographic record
Abstract
A Review of: Lown, C., Sierra, T., & Boyer, J. (2013). How users search the library from a single search box. College & Research Libraries, 74(3), 227-241. Retrieved from http://dx.doi.org/10.5860/crl-321 Abstract Objective – To identify how users use a single search box. Design – Transaction log analysis. Setting – A large research university in North Carolina, United States of America. Subjects – Search results from a customized single search box on the library’s home page, consisting of 739,180 searches and 655,388 hyperlink selections. Methods – The authors used custom logging software to generate transaction logs of all searches that took place over two semesters (August to December 2010 and January to May 2011) in QuickSearch, a custom-made, combined search application. The study tracked search queries and hyperlink selections, noting which modules in the discovery interface (articles, catalogue, databases, and others) were selected and, from these modules, which hyperlinks were clicked. Main Results – Transaction log analysis was conducted on over 739,000 searches during the two semesters and over 655,000 hyperlink selections from the results interface. The 20 most frequent queries made in QuickSearch were primarily for specific resources (database titles or journal titles), administrative information, and library services. The 153 most popular searches made up 10% of all searches. Hyperlinks to full-text articles (41.5%) and the catalogue (35.2%) accounted for about 76% of the links selected. About 23% of links selected were for other modules (e.g., FAQs, “best bets,” and journal titles). Hyperlinks that led directly to specific items were selected more frequently than hyperlinks to a full list of results. Conclusion – Analysis of user transaction logs suggests that users do not understand what is being searched in a combined search box and that search applications need to direct users more effectively to resources beyond the catalogue and article databases. Users attempt to access a wide range of information from a single search box, and the most commonly used modules in QuickSearch do not serve many of the most frequent queries. Many of the most common queries can be defined and addressed with a predefined list of results, improving the quality of results and the search experience for users. Ongoing evaluation and analysis of the search interface and subsequent optimization for the most frequent queries can improve user experience.
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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.019 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.012 | 0.027 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.082 | 0.121 |
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".