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Record W2124666827 · doi:10.5539/ass.v8n11p50

Ethical Issues between Workforce and Religious Conviction

2012· article· en· W2124666827 on OpenAlexvenueno aff
Mohamad Zaid Mohd Zin, Ahmad Faisal Mahdi, Azhar Abdul Rahman, Mohd Syahiran Abdul Latif, Rohaya Sulaiman, Nurul Khairiah Khalid, Nurfahiratul Azlina Ahmad, Ahamad Asmadi Sakat, Adi Yasran Abdul Aziz, Mohd Roslan Mohd Nor

Bibliographic record

VenueAsian Social Science · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Islamic Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHonestyConvictionRighteousnessFaithMoralityIslamContext (archaeology)Economic JusticeWorshipAccountabilityWorkforcePublic relationsSociologyEngineering ethicsEnvironmental ethicsLawPolitical scienceEpistemologyTheology

Abstract

fetched live from OpenAlex

Problem statement: This article enhances the ethical issues consider the relationship between religious life and work ethics. Approach: Malaysia aim to achieved full developed nation’s, requires a professional workforce, not only educated and innovative, but ethically, with integrity, accountability, dynamic and committed to continuously increasing Muslim professionalism. In the context of the development of Muslim professionals with a holistic and integrated, Muslims needs to withholding Tawheed, the fundamentals of faith, based on Al-Quran and Hadith. Manifestations in life of the practice which accounts for worship and morality need to be implemented. Results: Islamic moral character requires the emphasize that following five key parameters of Islamic behavior which is justice, trust, righteousness, the struggle towards self-improvement and keeping promises. Conclusion: The properties of trust at work, honesty, responsibility and integrity should be established in each of the Muslims. Each institution needs to be continued in the religious education and level of consciousness must be nurtured and enhanced.

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)0.028
metaresearch head score (Gemma)0.072
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.147

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.072
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0050.017
Scholarly communication0.0050.004
Open science0.0010.006
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0070.001

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 head0.045
GPT teacher head0.405
Teacher spread0.360 · 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 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

Citations4
Published2012
Admission routes1
Has abstractyes

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