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Record W1579248137 · doi:10.18438/b87k7f

Learning from Chatting: How Our Virtual Reference Questions Are Giving Us Answers

2010· article· en· W1579248137 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
venuePublished in a venue whose home country is Canada.

Bibliographic record

VenueEvidence Based Library and Information Practice · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsUniversity of GuelphSt. Jerome's University
FundersUniversity of Guelph
KeywordsComputer scienceService (business)Database transactionWorld Wide WebOnline discussionDigital referenceType of serviceComputer-assisted web interviewingPsychologyDatabase

Abstract

fetched live from OpenAlex

Objective - This research compares two types of online reference services and attempts to determine whether the same sorts of questions are being asked; which questions are being asked most often; and whether patron and staff behaviour is consistent or different in the two types of online reference sessions. Patron satisfaction with the two types of online reference services is also examined.
 
 Methods - The researchers reviewed over 1400 online reference transcripts, including 744 from Docutek virtual reference (VR) transactions and 683 from MSN chat reference (IM) transactions. The questions were classified according to categories of reference questions based on recurring questions discovered during the review. Each transaction was also categorized as "informal" or "formal" based on patron language and behaviour, and general observations were made about the interactions between patrons and librarians. In addition, results from 223 user surveys were examined to determine patron satisfaction with online reference services and to determine which type of service patrons preferred. 
 
 Results - The analysis suggests that patrons are using VR and IM services differently. In general, VR questions tend to be more research intensive and formal, while IM questions are less focused on academic research and informal. Library staff and patrons appear to alter their behaviour depending upon which online environment they are in. User surveys demonstrated that patrons are generally satisfied with either type of online reference assistance. 
 
 Conclusion - Both types of online reference service are meeting the needs of patrons. They are being used for different purposes and in different ways, so it may be worthwhile for libraries to consider offering both VR and IM reference. The relationship building that appears to take place more naturally in IM interactions demonstrates the benefits of librarians being more approachable with patrons in order to provide a more meaningful service.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0030.710
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.021
GPT teacher head0.284
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it