Virtual Reference in CARL Libraries and Institutions
Bibliographic record
Abstract
In 2002 a survey was sent out to 124 ARL Libraries probing the use of chat reference.Key issues addressed were training, software selection, staffing, organization and management.This survey provided a comprehensive analysis of the situation in American university libraries as it existed at that time.Our research was intended to probe the Canadian university library situation and also to discover what if anything had changed over the 4 yr period between the original survey and ours.The initial step was to gain permission from the original authors Jana Ronan, Associate University Librarian and Carol Turner, Associate Director, Public Services Division, both of the University of Florida to use their survey as the basis for the Canadian one.By repeating the survey the authors felt that valuable benchmark data would be gained in addition to simply acquiring information on chat reference in Canadian university libraries.With permission obtained 29 surveys were sent out to CARL institutions in May 2006.For the purpose of our study "chat reference" was "defined as immediate interactive reference delivered via computer and real-time communication software such as chat, instant messaging, and other conferencing media", the same definition as that used in the original survey.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.021 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.086 | 0.008 |
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".