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Record W2474040821 · doi:10.5539/ass.v12n7p47

Social Interactions among Multi-Ethnic Students

2016· article· en· W2474040821 on OpenAlexvenueno aff
Abdul Talib Mohamed Hashim, Noor Insyiraah Abu Bakar, Nordin Mamat, Abdul Rahim Razali

Bibliographic record

VenueAsian Social Science · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupNonprobability samplingPsychologySocial psychologyCompetition (biology)Social relationFocus groupSociologyDemography

Abstract

fetched live from OpenAlex

<p>Positive social interaction is a vital aspect of maintaining a harmonious condition, especially in Malaysia, a country which has a multiracial society. Therefore, this study was carried out to identify the patterns of social interactions among multiethnic students in national secondary schools in Malaysia. The respondents for this study comprise two school administrators, seven teachers and 20 students of various ethnicities. They were selected through purposive sampling. Data were collected through interview sessions either individually or in focus groups, observation and document analysis. The study’s findings showed that, there are five patterns of social interaction such as cooperation, exchange, competition, conflict and non-verbal communication among multiethnic students. Although there was conflict, the underlying causes of the conflict were not due to racial issues. As well as the five patterns of social interactions stated above, stereotypes were also reported among students. Despite conflicts and stereotypes, the students actively attempted to learn about other cultures and demonstrated attitudes of acceptance towards friends from other ethnic backgrounds. This indicates that the social interaction among students is still favorable and can be further improved through appropriate response. Additionally, based on these findings, a few recommendations were made regarding this issue.</p>

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.810
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0040.004
Scholarly communication0.0000.001
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 head0.075
GPT teacher head0.466
Teacher spread0.390 · 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; both teacher heads agree on what is shown here.

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

Citations6
Published2016
Admission routes1
Has abstractyes

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