Ready for it? Training Library School Graduate Students to Provide Reference Services
Bibliographic record
Abstract
While Master of Library and Information Science programs provide students with a space to explore many theoretical subjects, few opportunities for practical experience are offered. At a large research university in Canada, the Library has created a program to hire graduate students in Library and Information Science and train them to provide reference services. Students receive training on the reference interview, subject-specific tools, the virtual reference platform, and are expected to complete several hours of shadowing with experienced librarians. The program presents a unique occasion for students to not only receive formal training but to also benefit from informal mentorship from librarians in different subject areas. Once the training is completed, students provide reference assistance to a large student population in person and through virtual reference (chat, email, and text). Once they are comfortable with reference, the graduate students are also provided with opportunities to co-teach information literacy instruction sessions to gain valuable teaching experience. They can also be called upon to complete special projects with librarians such as book displays or library subject guides. This presentation will highlight how to create job opportunities for future librarians and provide an overview of the training program, with a focus on the practical skills needed to offer reference services. Special attention will be given to how academic libraries can provide the practical experience in reference and instruction that can be absent in the Master’s program.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.068 | 0.036 |
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".