Innovations in Discovery Systems: User Studies and the Bento Approach
Bibliographic record
Abstract
Over the past 30 years, library discovery services have evolved through expanded OPACs, federated search systems employing broadcast searching; Web-scale discovery systems (WSDS) that aggregate metadata and full-text content into a single integrated index; and, currently, hybrid bento-style systems that use federated techniques over WSDS, OPACs, and local information content. The bento systems partition search results into separate zoned screen displays grouped by content format type and/or local service results. Recent studies on Web-scale discovery systems have identified a number of user access issues centering on problems with blended result displays, problematical relevancy rankings of search results, full-text search problems, and the inability of WSDS to adequately provide access to local library services and resources. The concept of “full library discovery,” a phrase first coined by Lorcan Dempsey, has been introduced to refer to discovery approaches that move beyond the retrieval of collection materials to also include local information services and local content and links. The bento-based systems are an attempt to address the identified problems with WSDS and also provide discovery services that address user needs, in particular known item search and streamlined full-text access. This presentation will provide an analysis of the 38 libraries presently employing the bento approach and will look at identified user needs and search behaviors, as revealed in detailed search and clickthrough transaction log analyses. There is a clear need for an evidence-based analysis of user search behaviors in retrieval environments characterized by access to distributed information resources.
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 | 0.034 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".