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Record W2774129038 · doi:10.1145/3134686

Two Sides to Every Story

2017· article· en· W2774129038 on OpenAlexaboutno aff
Helen Ai He, Naomi Yamashita, Chat Wacharamanotham, Andrea B. Horn, Jenny Schmid, Elaine M. Huang

Bibliographic record

VenueProceedings of the ACM on Human-Computer Interaction · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsnot available
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsIntercultural communicationNegotiationSalientTask (project management)PerceptionComputer-mediated communicationSocial psychologyPsychologyCommunicationComputer scienceSociologyWorld Wide WebEngineering

Abstract

fetched live from OpenAlex

Global virtual teams experience intercultural conflict. Yet, research on how Computer-Mediated Communication (CMC) tools can mitigate such conflict is minimal. We conducted an experiment with 30 Japanese-Canadian dyads who completed a negotiation task over email. Dyads were assigned to one of three conditions: C1) no feedback; C2) automated language feedback of participant emails based on national culture dimensions; and C3) automated language feedback (as in C2), and participants' shared self-reflections of that feedback. Results show Japanese and Canadian partners interpreted the negotiation task differently, resulting in perceptions of intercultural conflict and negative impressions of their partner. Compared to C1, automated language feedback (C2) and shared self-reflections (C3) made cultural differences more salient, motivating participants to empathize with their partner. Shared self-reflections (C3) served as a meta-channel to communication, providing insight into each partner's intentions and cultural values. We discuss implications for CMC tools to mitigate perceptions of intercultural conflict.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.369
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.105
GPT teacher head0.416
Teacher spread0.311 · 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.

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

Citations15
Published2017
Admission routes1
Has abstractyes

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