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Record W4410780541 · doi:10.1002/crq.21491

Managing Conflict Resolution and Perceptions: An Approach Leveraging the Thomas‐Kilmann Conflict Mode Theory

2025· article· en· W4410780541 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.

Bibliographic record

VenueConflict Resolution Quarterly · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsConcordia University
Fundersnot available
KeywordsConflict resolutionPerceptionMode (computer interface)Social psychologyConflict theoriesPsychologyConflict managementResolution (logic)SociologyPolitical scienceComputer scienceSocial scienceArtificial intelligenceHuman–computer interaction

Abstract

fetched live from OpenAlex

ABSTRACT Conflict resolution management is a process whereby two or more parties engage toward an agreeable solution to a dispute. In this study, we propose a methodology to start a structured process of resolving complex conflicts between parties that previously avoided any form of engagement. We leverage the Thomas‐Kilmann conflict mode theory to guide our methodological design that we have termed SACRE (Symmetric Asynchronous Conflict Resolution Environment). SACRE was designed for and implemented in a real‐world project related to the complex topic of the Israeli–Syrian conflict. We then tested the effectiveness of SACRE in an academic setting and examined through statistical analysis its impact on the understanding and the perceptions of the conflict. Our findings indicate that SACRE, which offers an approach to communication among parties in conflict, can enhance knowledge of the issues of contention and bridge the conflict's perceptual gap.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.001
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 head0.034
GPT teacher head0.318
Teacher spread0.284 · 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