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Record W2041317704 · doi:10.1002/crq.235

Building trust with parties: Are mediators overdoing it?

2009· article· en· W2041317704 on OpenAlexaff
Arnaud Stimec, Jean Poitras

Bibliographic record

VenueConflict Resolution Quarterly · 2009
Typearticle
Languageen
FieldSocial Sciences
TopicConflict Management and Negotiation
Canadian institutionsHEC Montréal
Fundersnot available
KeywordsMediationMediatorSet (abstract data type)Social psychologyConflict resolutionPoint (geometry)PsychologyPolitical scienceComputer scienceLawMathematics

Abstract

fetched live from OpenAlex

Abstract Trust is a key factor in the dynamics of any attempt to settle a conflict. A mediator may be the needed link between the parties, as long as they trust their mediator. But how far should mediators go to win parties' trust? On the basis of questionnaires filled out by participants in employer‐employee mediation, we arrive at a conclusion that differs from the prevailing wisdom. There is a threshold point rather than a linear relationship between the level of trust in the mediator and the degree of conflict resolution. Once the threshold has been reached, additional trust does not necessarily result in a higher level of conflict resolution. Possible explanations are set out and practical implications are discussed.

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)0.029
metaresearch head score (Gemma)0.114
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.029
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0290.114
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0060.010
Scholarly communication0.0110.010
Open science0.0010.007
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.022
GPT teacher head0.294
Teacher spread0.272 · 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 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

Citations65
Published2009
Admission routes1
Has abstractyes

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