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Record W2154164419 · doi:10.22146/gamaijb.5565

Social and Environmental Reporting and Auditing in Indonesia: Maintaining Organizational Legitimacy?

2005· article· en· W2154164419 on OpenAlexaff
Anies Said Basalamah, Johnny Jermias

Bibliographic record

VenueGadjah Mada International Journal of Business · 2005
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAuditEnvironmental reportingBusinessLegitimacyAccountingEnvironmental accountingPublic relationsSocial responsibilityEnvironmental auditPolitical science

Abstract

fetched live from OpenAlex

The purpose of this study is to examine social and environmental reporting and auditing practices by companies in Indonesia. Consistent with our prediction, we found that social and environmental reporting and auditing are undertaken by management for strategic reasons, rather than on the basis of any perceived responsibilities. The results indicate that reporting and auditing social and environmental activities increases following threats to the company’s legitimacy and ongoing survival. The results also support our prediction that social and environmental reports vary across companies. This study calls for mandatory reporting and auditing of social and environmental activities through regulations and reinforcements. This mandatory requirement is particularly needed for companies with activities that are considered socially and environmentally sensitive. Furthermore, this study reveals that the social and environmental reporting and auditing are performed by organizations other than accounting profession. We propose that accountants should partake in these activities given the expertise that they could usefully bring to these areas.

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)0.007
metaresearch head score (Gemma)0.021
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 score0.015
Threshold uncertainty score0.035

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0020.004
Scholarly communication0.0050.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.263
Teacher spread0.240 · 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

Citations86
Published2005
Admission routes1
Has abstractyes

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