Bibliographic record
Abstract
A Review of: Olson, Tod A. “Utility of a Faceted Catalog for Scholarly Research” Library Hi Tech 25.4 (2007): 550-61. Objective – To learn whether a faceted catalogue and word cloud aids in the discovery process. Design – User study. Setting – Large academic research library in the United States. Subjects – Twelve PhD candidates in the humanities, the majority of whom are engaged with researching, proposing, or writing their dissertations. Methods – The library’s entire catalogue of 5.2 million records was loaded into the AquaBrowser OPAC search interface. A pilot study was conducted using three humanities graduate students employed by the library. Following the pilot, the main study was conducted using graduate students in the humanities. Graduate students in the social sciences were desired for the study, but were not able to be contacted due to time constraints. Once selected, the test subjects were asked to use an interface that offered both facets and tag clouds for enhanced search quality. Test subjects were allowed to choose the topic they would like to research; all chose to research their dissertation topic. A moderator and recorder facilitated research conducted with the faceted catalogue. The moderator ensured that students commented on their findings, cleared up any confusion with using the interface, and kept the students on task. Only when students remarked that a new discovery had been made were those discoveries noted. The impact to the discovery process of faceted navigation and AquaBrowser’s word cloud was studied while the impact of relevance ranking was not. Main Results – The article asserts that results from both the pilot and main study were sufficiently similar to justify combining them for the paper, but the advantage that students employed by the library might have over other students is not discussed. Nine of the twelve students used in the study found new results using the faceted catalogue and word cloud. The responses of the user group to the faceted catalogue and word cloud were “overwhelmingly positive” (555). However, since students were allowed to move freely between the word cloud and faceted navigation tool, it is difficult to attribute new discoveries solely to one or the other. However, when a new discovery could be “attributed primarily to one factor or another” (555) it was noted. The faceted navigation tool aided discovery at least four times and the word cloud aided discovery at least six. Conclusion – A faceted catalogue interface with a word cloud feature clearly aids in the discovery process for more advanced researchers—those with specialized subject knowledge, familiarity with their library’s collection, and experience in researching their area. However, facets and word clouds have limitations: records with limited cataloguing have little to offer faceted navigation; catalogue records from diverse providers introduce controlled vocabularies beyond LCSH and MeSH into search returns, resulting in the same word potentially appearing multiple times in the same return albeit with different meanings; the word cloud may contain certain words that researchers feel to be irrelevant. Despite these issues, the use of word clouds and faceted navigation (and relevance ranking) appears to be beneficial to research conducted by experienced subject searchers in the humanities.
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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.014 | 0.018 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.042 | 0.028 |
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".