Bibliographic record
Abstract
This exploratory study investigated the help-seeking preferences of library users at two large urban universities in Toronto. Reference desk and virtual reference users were compared in terms of their perceptions of the options now available for obtaining reference help. The premise for the study was based on the assumption that a reasonable exposure to newer reference services, such as chat and e-mail, had occurred, therefore allowing for an examination of emerging preferences for different types of services. Surveys were distributed to both reference desk and virtual reference users asking seven core questions exploring use and preference for reference services as well as habits and preferences for study location (in library, off campus, etc.). The results suggest that the reference desk continues to be the most popular method of getting help in the library, but virtual reference satisfies a niche for users who prefer to work outside the library. Those who use virtual reference tend to perceive their options for getting help differently from other users. Virtual reference users do not perceive virtual reference as a novelty or as a marginal service, but see it as a significant service option. In addition, the results show that virtual reference services may have a special appeal to graduate students since graduate students seem more likely to conduct their research outside the library. The study concludes with recommendations for planning and for future research.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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".