Bibliographic record
Abstract
Objective – The purpose of this study was to analyze the data from a reference statistics-gathering mechanism at Colorado State University (CSU) Libraries. It aimed primarily to better understand patron behaviours, particularly in an academic library with no reference desk. Methods – The researchers examined data from 2007 to 2010 of College Liaison Librarians’ consultations with patrons. Data were analyzed by various criteria, including patron type, contact method, and time spent with the patron. The information was examined in the aggregate, meaning all librarians combined, and then specifically from the Liberal Arts and Business subject areas. Results – The researchers found that the number of librarian reference consultations is substantial. Referrals to librarians from CSU’s Morgan Library’s one public service desk have declined over time. The researchers also found that graduate students are the primary patrons and email is the preferred contact method overall. Conclusion – The researchers found that interactions with patrons in librarians’ offices – either in person or virtually – remain substantial even without a traditional reference desk. The data suggest that librarians’ efforts at marketing themselves to departments, colleges, and patrons have been successful. This study will be of value to reference, subject specialist, and public service librarians, and library administrators as they consider ways to quantify their work, not only for administrative purposes, but in order to follow trends and provide services and staffing accordingly.
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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 | 0.025 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".