Bibliographic record
Abstract
Objective – To discover students’ perceptions of information commons staff, and to determine how these perceptions influence the use of library resources. Design – Post-experience survey with one follow-up interview. Setting – The University of Sheffield, a post-secondary institution in England. Subjects – All undergraduate and postgraduate students were invited to take part. Just over 1% of the student population, or 250 students, completed the survey. Methods – Information about the survey was sent to students’ institutional email addresses. One follow up interview was carried out via email using the critical incident technique. Main Results – Students do not understand the academic roles of librarians. They are unlikely to approach library staff for academic support, preferring to turn to instructors, other students, friends, and family. Most students had positive opinions about assistance received in the Information Commons, but a small number reflected on previous bad experiences with staff, or on a fear of being made to feel foolish. The vast majority of students who did not seek help in the Information Commons stated that this was because they did not require assistance. Most students do not perceive a difference between Information Commons staff and library staff. Conclusion – Students have positive views of Information Commons staff at the University of Sheffield, but have low awareness of the roles of professional librarians. Librarians need to develop partnerships with academic staff and strengthen their presence in both physical and online learning environments to promote their academic roles.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.006 |
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".