Roaming Reference: Reinvigorating Reference through Point of Need Service
Bibliographic record
Abstract
Roaming reference service was pursued as a way to address declining reference statistics. The service was staffed by librarians armed with iPads over a period of six months during the 2010-2011 academic year. Transactional statistics were collected in relation to query type (Research, Facilitative or Technology), location and approach (librarian to patron, patron to librarian or via chat widget). Overall, roaming reference resulted in an additional 228 reference questions, 67% (n=153) of which were research related. Two iterations of the service were implemented, roaming reference as a standalone service (Fall 2010) and roaming reference integrated with traditional reference desk duties (Winter 2011). The results demonstrate that although the Weller Library’s reference transactions are declining annually, they are not disappearing. For a roaming reference service to succeed, it must be a standalone service provided in addition to traditional reference services. The integration of the two reference models (roaming reference and reference desk) resulted in a 56% decline in the total number of roaming reference questions from the previous term. The simple act of roaming has the potential to reinvigorate reference services as a whole, forcing librarians outside their comfort zones, allowing them to reach patrons at their point of need.
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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 | 0.022 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.011 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".