Structure of domain novice users' queries to a history database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it