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Record W2099418281 · doi:10.5539/ass.v9n8p9

The Role of Values and Attitudes in Political Participation

2013· article· en· W2099418281 on OpenAlexvenueno aff
Suhana Saad, Ali Salman

Bibliographic record

VenueAsian Social Science · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsnot available
FundersUniversiti Kebangsaan Malaysia
KeywordsMalayPoliticsEthnic groupPolitical cultureValue (mathematics)Political scienceSociologyEconomic growthEconomicsLaw

Abstract

fetched live from OpenAlex

This research focuses on political culture with a special reference to political participation of the Malays and the non-Malays in district of Hulu Langat, Malaysia. Political landscape in Malaysia is normally being observed in term of ethnicity. This is why political observers in Malaysia claim that the goal of development and individual’s behavior towards politics are habitually govern by ethnicity. Therefore, this paper aims to scrutinize the significance of value and attitude in political participation between Malay and non-Malay. These two vital factors are constanly being neglected in observing political participation. Value and attitude are also positively involved with conventional and non-conventional political participation, while socio-economic status only acts as an underpinning principle. The research was held in Hulu Langat District in Malaysia and rationally being chosen due to its ethnic composition is similar to Malaysian’s ethnic composition, namely Bumiputera, Chinese and Indian. Data was collected through qualitative method that emphasized political participation and respondents’ perspectives on politics and leadership. A total of 400 respondents were sampled involving 208 Malays, 148 Chinese and 44 Indians. The pattern of political participation in this area can explain the politics scenario or political culture among multi-ethnic society in Malaysia.

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)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.018
GPT teacher head0.343
Teacher spread0.325 · 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 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

Citations10
Published2013
Admission routes1
Has abstractyes

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