Internet Reference Services and the Reference Desk: Does the Nature of a User's Query Really Change?
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 reviews the nature of queries posed by library patrons during a regular reference interview, within the conceptual framework of Internet reference services in an academic library. The users of the reference desk in a typical university setting usually range from the freshmen to the faculty, with researchers from the corporate sector occasionally dropping in. Presented here will be the viewpoint of a reference librarian indicating that library users, armed with the newly acquired knowledge of Internet/electronic tools, leave the reference desk with a vastly different perspective on carrying out a literature search than they arrived with. The observation is that while the basic theme and nature of queries remain pretty much the same, it is the role of the reference librarian in responding to these queries that has undergone an immense change.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.008 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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