A Holistic Look at Reference Statistics: Whither Librarians?
Bibliographic record
Abstract
Abstract Objective – Washington State University (WSU) Pullman campus librarians track a diverse set of reference statistics to gain a “holistic” look at local reference transaction trends. Our aim was to aggregate virtual, reference desk and office transaction data over the course of three years to determine staffing levels. Specifically, we asked “Where should reference librarians be to answer questions?” Methods – Using Springshare’s LibAnalytics, we generated longitudinal (2012-2014) statistics and data, to help us assess the patterns and trends of patron question numbers, types, communication modes, and locations in the Terrell Library. With this data, we considered current staffing patterns and how we could best address patron needs. Results – Researchers found that compiling data across modalities of location, communication, question type, and the READ Scale led to a better understanding of user behavior trends. Conclusion – Examining and interpreting a more inclusive and richer set of transaction statistics gives reference managers a better picture of how patrons are seeking help, and can serve as a basis for making staffing decisions.
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 | 0.035 | 0.115 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.018 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.018 | 0.039 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".