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Record W2047590832 · doi:10.5539/ach.v1n2p113

Socio-Economic Issues among Felda Settlers in Perlis

2009· article· en· W2047590832 on OpenAlexvenueno aff
Bahijah Md Hashim, Adilah Abdul Hamid, Mat Saad Abdullah, Rohana Alias, Muhamad Noor Sarina

Bibliographic record

VenueAsian Culture and History · 2009
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)ProductivityPhenomenonFace (sociological concept)Order (exchange)GeographyWork (physics)Demographic economicsEconomic growthEconomicsSociologyEngineeringSocial science

Abstract

fetched live from OpenAlex

After almost fifty years of operation, government through a number of announcements declared that FELDA (Federal Land Development) schemes need to be revitalized so that it could play its role more effectively as a vehicle that would accelerate the country’s economic growth. Having raised this point, the major aim of this study is to examine the major socio-economic issues and the current socio-economic status of FELDA settlers.Information was gathered through face-to-face interview with the Mata Air FELDA settlers and the Rimba Mas FELDA settlers in Perlis. Descriptive statistics such as mean, standard deviation and percentages were employed to describe the socio-economic problems and issues studied. The findings of the study indicate the implication of the ageing phenomenon of the FELDA settlers in Perlis to some extent affect the settlers’ ability to work effectively on their plantation. It is also found that the second generation issue has become the most significant factor contributing to the productivity and income increment of the FELDA settlers in Perlis as compared to other selected socio-economic variables. The result also suggested that, the ageing phenomenon, second generation issue and entrepreneurship problem must be seriously taken into consideration in order to accelerate the FELDA’s growth specifically and the country’s growth generally.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.740
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.014
GPT teacher head0.195
Teacher spread0.181 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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
Published2009
Admission routes1
Has abstractyes

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