Exploratory Search and Beyond: A Study of Complex Search Scenarios within a Public Digital Library
Bibliographic record
Abstract
ABSTRACT Complex search scenarios and exploratory search processes are understudied within the context of public digital library systems. While the literature indicates that exploratory search is effective when undertaking complex search scenarios, the primary focus has been on searching conducted by students, academics, or professionals. In this paper, our focus is on whether public library patrons use such approaches and whether they have developed other ways to pursue complex search scenarios. Using retrospective think‐aloud protocols, nine participants first pursued a complex search scenario of their choosing without intervention, and then narrated what they were thinking and doing while watching a recording of their search. Thematic analysis was used to identify patterns within the data. Three themes were identified and examined in detail: exploratory search, planning, and pragmatism. The key finding is that while the exploratory search model matches some of the cognitive processes described by the participants, other processes were also employed. In particular, we identified both the metacognitive aspect of planning and the strategic decision‐making aspect of pragmatism. This work provides evidence of the need to expand the exploratory search model to include metacognitive and strategic decision‐making aspects.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".