Developing and assessing a graduate student reference service
Bibliographic record
Abstract
Purpose The purpose of this paper is to formally assess the training program received by information studies graduate students and the reference services they provided at a research-intensive university. Design/methodology/approach A qualitative content analysis was used to evaluate if graduate students incorporated the training they received in their provision of reference services. The students’ virtual reference transcripts were coded to identify the level of questions asked, if a reference interview occurred and if different teaching methods were used by the students in their interactions. The in-person reference transactions recorded by the students were coded for the level of questions asked. Findings The main findings demonstrate a low frequency of reference interviews in chat interactions with a presence in only 23 per cent of instances while showing that instructional methods are highly used by graduate student reference assistants and are present in 66 per cent of chat conversations. Originality/value This study is of interest to academic libraries who wish to partner with information studies programs and schools to offer graduate students valuable work experience. It aims to show the value that graduate students can bring to reference services. Furthermore, it highlights the importance of continuously developing training programs and assessing the performance of graduate students working in these roles.
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.
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 | 0.050 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".