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Record W4395961798 · doi:10.18280/jesa.570226

Development and Measurement of Six Capitals in Automotive Parts Manufacturing Companies in Indonesia and Malaysia

2024· article· en· W4395961798 on OpenAlexvenueno aff
Hendra Hendra, Khomsiyah, Juniatí Gunawan

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryBusinessManufacturing engineeringEngineering

Abstract

fetched live from OpenAlex

This study aims to develop a six-capital measurement in the company value creation process and its implementation in the automotive parts manufacturing Industry in Indonesia and Malaysia.This study develops a six-capital measurement in the company value creation process that aligns with sustainable development goals.This study is qualitative in nature.The approach used in this research is a case study.This research uses three types of informants by conducting in-depth interviews so that each need can be properly explored.Three types of informants: regulators, academics, and practitioners in the field of sustainability reporting, audit and integrated reporting, consultants, assurors, and company actors who made sustainability reports and integrated reports with a total of eight informants.This study concludes that the proposed measurement can be used to measure the six types of capital needed in the company value creation process in the automotive parts manufacturing sector.

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)0.003
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 score0.007
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.039
GPT teacher head0.231
Teacher spread0.192 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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