Bibliographic record
Abstract
Objective - The primary purpose of this study was to better understand the nature of “reference” and reference transactions. Methods - This study looked at four years’ of reference transaction (RT) data recorded at a small, state-owned university. Results - The data clearly indicates that the overall number of RT continues to decline. It also reveals that, despite the use of student mentors, librarians are still involved with a majority of RT, regardless of whether or not they require the expertise of a librarian to resolve. Conclusion - Continuing to be involved with RT which do not require the knowledge or training of a librarian (e.g., directional) can have a diminutive effect on the perceived role, work, and value of librarians. As such, it is suggested that these sorts of questions be addressed by student mentors or staff members. In turn, this will allow librarians to focus on those questions and activities which do require their unique knowledge and skills. Along similar lines, it is also suggested that librarians explore and identify new, non-traditional ways of applying their expertise to student success initiatives and the overall academic life of the institution. With the merger of three libraries, data from this study has been and continues to be used to make informed decisions about the provision of reference services in a new, integrated library environment.
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 | 0.016 | 0.106 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.016 | 0.027 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".