Structure of domain novice users' queries to a history database
Bibliographic record
Abstract
Abstract This paper presents an information need identification system for interactive information retrieval (IR) for undergraduates researching a history topic, called the INIIReye System. The overall purpose of the INIIReye System is to facilitate domain novice user identification of their information need while they are online interacting with the information store. Here, we give preliminary results from a study that narrows undergraduates' initial topic statements to information need statements. Students may use a faulty accessing point in their queries because before information need identification they base their queries on broad topic terms. We first categorize the type of query terms used by users of an historical database provider, to create a taxonomy of query terms. Next, we use a case study of a history student who visually represents the narrows his essay topic in a series of steps. We conclude that our query taxonomy must include levels of topic specificity because while general topic‐based queries are inappropriate as query terms, more specific topic‐based queries may be closer to the domain novice user's real information need.
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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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".