The long tail of search and topical queries in public libraries
Bibliographic record
Abstract
Purpose The purpose of this paper is to elaborate the long tail of topical search queries, including the influence of current events, posed to a large, urban public library discovery system. Design/methodology/approach Search queries from the months of June, July, August and September 2014 (1,488,339 total queries) were collected from the Edmonton Public Library’s BiblioCommons database using Google Analytics and exported to Excel. The data were then analyzed using descriptive statistics, frequency counts and textual analysis to explicate the long tail of search, (including the most popular searches) and to explore the relationship between topical search queries and current events. Findings The findings support the long tail theory, as the aggregate tail of topical search queries comprised the vast majority of the total searches and current events exert some influence on the nature and frequency of topical searches. Research limitations/implications Data collection was limited to four months of the year; thus, comparisons across the year cannot be made. There are practical implications for public libraries in terms of marketing and collections, as well as for improving catalogue functionality, to support user search behaviour. Originality/value Not much research attention has been focused on the nature of topical search queries in public libraries compared to academic libraries and the Web. The findings contribute to developing insight into the divergent interests of intergenerational public library users and the topics of materials they are searching for.
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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 | 0.009 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".