Evaluating citation visualization and exploration methods for supporting academic search tasks
Bibliographic record
Abstract
Purpose – Conducting academic searches within online digital libraries can be a difficult task due to the complexity of the searcher’s information need. The interfaces for such digital libraries commonly use simple search features that provide limited support for the fundamental strategies that academic searchers employ. The authors have developed a novel visualisation interface called Bow Tie Academic Search to address some of these shortcomings, and present in this paper the findings from a user evaluation. The paper aims to discuss these issues. Design/methodology/approach – A controlled laboratory study was conducted to compare a traditional search interface to Bow Tie Academic Search. In total, 24 graduate students were recruited to perform academic searches using the two candidate interfaces, guided by specific sub-tasks that focus on citation and keyword analysis strategies. Findings – Although the use of the core visualisation and exploration features did not reveal differences in retrieval effectiveness or efficiency, the query refinement features were found to be effective. Strongly positive impressions of usefulness and ease of use of all aspects of the system were reported, along with a preference for using Bow Tie Academic Search for academic search tasks. Originality/value – This study provides insight into the potential value for providing visual and interactive interfaces for supporting academic search tasks and strategies. While the quantitative improvements over the traditional search interface were minimal, the qualitative measures illustrate the value of Bow Tie Academic Search.
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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 | 0.019 | 0.129 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".