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Record W1521489702 · doi:10.17705/1jais.00113

Do You Read Me? Perspective Making and Perspective Taking in Chat Communities

2007· article· en· W1521489702 on OpenAlexfundno aff
Michael H. Dickey, Gary Burnett, Katherine M. Chudoba, Michelle M. Kazmer

Bibliographic record

VenueJournal of the Association for Information Systems · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersMcGill UniversityFlorida State University
KeywordsPerspective (graphical)Context (archaeology)VocabularyComputer scienceService (business)Process (computing)Knowledge managementValue (mathematics)LinguisticsMarketingBusinessArtificial intelligence

Abstract

fetched live from OpenAlex

We present a study of synchronous, text-based chat communications between customers and customer service representatives (CSRs), and examine the process of coordinating perspectives through perspective making and perspective taking to build shared understanding of context. Using a cultural hermeneutic lens and its four contextual relations, we studied more than 4400 chat messages generated during a two-year period. Successful coordination of perspectives occurred in eighty percent of the exchanges, in spite of conversational incoherence introduced by the chat technology. When coordination of perspectives between customers and CSRs failed, it was due to one or a combination of three factors: the customer's inability to successfully communicate intention, lack of customer/CSR shared understanding of reference about what was being discussed, and/or misinterpretation of each other's identities. This suggests that technology solutions to reduce conversational incoherence may not be of as much value as improving how people articulate intention and create shared reference. Finally, we demonstrate that contextual relations in cultural hermeneutics offer an analytic device and vocabulary to discern exactly what is missing when technology-mediated communication breaks down.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.703
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0000.000
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 head0.033
GPT teacher head0.295
Teacher spread0.263 · 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 designQualitative
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

Citations38
Published2007
Admission routes1
Has abstractyes

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