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Record W4234768989 · doi:10.5860/0700071

Measuring Quality in Chat Reference Consortia: A Comparative Analysis of Responses to Users’ Queries

2009· article· en· W4234768989 on OpenAlexaff
Deborah Meert, Lisa M. Given

Bibliographic record

VenueCollege & Research Libraries · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicLibrary Science and Information Literacy
Canadian institutionsMcGill UniversityUniversity of Alberta
Fundersnot available
KeywordsService (business)Quality (philosophy)World Wide WebComputer scienceKey (lock)Service qualityAcademic libraryBusinessLibrary scienceMarketing

Abstract

fetched live from OpenAlex

Academic libraries have experienced growing demand for 24/7 access to resources and services. Despite the challenges and costs of chat reference service and consortia, many libraries are finding the demand for these services worth the cost. One key challenge is providing and measuring quality of service, particularly in a consortia setting. This study explores the quality of service provided in one academic library participating in a 24/7 chat reference consortium, by assessing transcripts of chat sessions using in-house reference quality standards. Findings point to both similarities and differences between chat interactions of local librarians versus consortia staff.

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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.824
Threshold uncertainty score0.915

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.009
Science and technology studies0.0010.001
Scholarly communication0.0000.013
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.375
GPT teacher head0.475
Teacher spread0.100 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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".

Quick stats

Citations6
Published2009
Admission routes1
Has abstractyes

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