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Record W2759743818 · doi:10.5539/ass.v13n10p95

Conflict Management among Malay Married Couples: An Analysis on Their Strategies & Tactics

2017· article· en· W2759743818 on OpenAlexvenueno aff
Nor Hafizah Abdullah, Nor Azlili Hassan, Abdul Satar Abdullah Harun, Liana Mat Nayan, Rahilah Ahmad, Madihah Md. Rosli

Bibliographic record

VenueAsian Social Science · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMalayCompromiseConflict managementPoliticsQuality (philosophy)SociologySocial psychologyPsychologyPublic relationsPolitical scienceLawSocial science

Abstract

fetched live from OpenAlex

The purpose of this study is to explore the strategies and tactics used in conflict management and analyze their effectiveness based on quantitative methodology. Probability sampling of 300 respondents in Selangor, Malaysia consisting of Malay married couples were selected using cluster sampling. The findings showed that the strategies were competing, collaborating, compromising, avoiding and accommodating. In average, around 80 percent of Malay married couples chose collaborating strategy whereas competing was less popular. However, the most popular tactic among the respondents is trying to do what is necessary to avoid tension which is under the avoiding strategy. Two-way communication and compromise were seen to be the essence in keeping longevity and success in marriage. The study revealed that there was a change in conflict management among Malay married couples which can be related to the economic development of society, technological advances, political scenarios and the influx of foreign culture. Nonetheless, along with the changes in Malaysia’s economic system, modern Malay couples are more open-minded. Therefore, couples in this study tend to see conflicts as problems that need to be solved, wanting quality decisions that truly resolve the issues. They believe in the power of consensus and in sharing of information and achieving understanding with one another.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.903
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.001
Scholarly communication0.0020.002
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 head0.027
GPT teacher head0.286
Teacher spread0.260 · 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 designObservational
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

Citations13
Published2017
Admission routes1
Has abstractyes

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