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Record W2729717268 · doi:10.18438/b85370

Implementation of Proactive Chat Increases Number and Complexity of Reference Questions

2017· article· en· W2729717268 on OpenAlexvenueno aff
Sue F. Phelps

Bibliographic record

VenueEvidence Based Library and Information Practice · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsnot available
Fundersnot available
KeywordsStaffingReference deskComputer scienceService (business)Service deskPoint (geometry)World Wide WebCategorizationEmpirical researchPsychologyMedicineService delivery frameworkService designStatisticsArtificial intelligenceNursingMathematics

Abstract

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A Review of: Maloney, K., & Kemp, J. H. (2015). Changes in reference question complexity following the implementation of a proactive chat system: Implications for practice. College & Research Libraries, 76(7), 959-974. http://dx.doi.org/10.5860/crl.76.7.959 Abstract Objective – To determine whether the complexity of reference questions has changed over time; whether chat reference questions are more complex than those at the reference desk; and whether proactive chat increases the number and complexity of questions. Design – Literature review and library data analysis. Setting – Library of a doctoral degree granting university in the United States of America. Methods – The study was carried out in two parts. The first was a meta-analysis of published data with empirical findings about the complexity of questions received at library service points in relationship to staffing levels. The authors used seven studies published between 1977 and 2012 from their literature review to create a matrix to compare reference questions based on the staffing level required to answer the questions (e.g., by a nonprofessional, a generalist, or a librarian). They present these articles in chronological order to illustrate how questions have changed over time. They sorted questions by the service point at which they were asked, either through chat service or at a reference desk. In the second part of the study authors used the READ scale to categorize the complexity of questions asked at the reference desk and via proactive chat reference. They collected data for chat reference for six one-week periods over the course of eight months to provide a representative sample. They recorded reference desk questions for three of those same weeks. Both evaluators scored the data for a single week to norm their results, while the remaining data was coded independently. Main Results – The complexity of questions in the seven articles studied indicated change over time, shown in tables for desk and chat reference. One outlier, a study published in 1977 before reference tools and resources moved online, reported that 62% of questions asked could be answered by nonprofessionals, 38% by a trained generalist, and only 6% required a librarian. The six other studies were published after 2001 when most resources had moved online. Of the questions from these six, authors found a range of 74-90% could be answered by a non-professional, 12-16% by a generalist, and 0-11% required a librarian. Once chat reference was added there was more variation reported between studies, with generalist questions at 30-47% of those reported and 10-23% requiring a librarian. Though the underlying differences in the study designs do not allow for formal analysis, the seven studies indicate that more complex questions are asked via chat service than at the reference desk. Each staffing level was grouped and averaged for comparison. The 1977 study shows nonprofessional questions at 62%, generalist questions at 32%, and librarian questions at 6%. Reference desk questions in the post-2001 articles indicated 81% nonprofessional, 13% generalist, and 5% librarian questions. Post-2001 chat questions were at 49% nonprofessional, 36% generalist, and 15% at librarian level. In the second part of the study, the data coded using the READ scale and collected from the proactive chat system showed an increased number and complexity of questions. The authors identified 4% of questions were rated at a level 1 (e.g., directional, library hours), 30% at level 2 (e.g., known item searching), 39% at level 3 (e.g., reference questions), and 27% at level 4 requiring advanced expertise (e.g., using specialized databases or data sets). Authors combined questions at levels 5 and 6 due to low numbers, and did not describe these when reporting their study. In comparison, 15% of reference desk questions were at a level 3 on the READ scale, and 1% were at level 4. Conclusion – Proactive chat reference service increased the number and the complexity of questions over those received via the reference desk. The frequency of complex questions was too high for nonprofessional staff to refer questions to librarians, causing reevaluation of the tiered service model. Further, this study demonstrates that users still have questions about research, but for users to access services for these questions “reference service must be proactive, convenient, and expert to meet user expectations and research needs” (p. 972).

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 imitation

Not 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.

metaresearch head score (Codex)0.065
metaresearch head score (Gemma)0.281
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.342

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0650.281
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0050.007
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.062
GPT teacher head0.376
Teacher spread0.314 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations0
Published2017
Admission routes1
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