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Record W2899254438 · doi:10.32812/jibeka.v12i1.17

OPINION ANALYSIS GOING CONCERN THROUGH AUDITOR QUALITY AND AUDITOR EXPERIENCE

2018· article· ca· W2899254438 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJurnal Ilmiah Bisnis dan Ekonomi Asia · 2018
Typearticle
Languageca
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsAuditTest (biology)Auditor's reportAffect (linguistics)AccountingQuality auditQuality (philosophy)Sample (material)PsychologyGoing concernAudit substantive testPopulationReliability (semiconductor)BusinessExternal auditorInternal auditDemographySociology

Abstract

fetched live from OpenAlex

The research was conducted in Malang City area with auditor as the population while the sample with pursposive sampling while the data analysis used is variable test with linkert scale and using SPPS statitisk test. In this study conducted at the Public Accounting Firm in Malang, showed that by using validity test, reliability, assumption Heterokedastisitas, multicolinierity assumption test, hypothesis testing simultaneously and partial and autocorrelation test showed that the questionnaire used by the researchers is feasible as a measuring tool for analysis in this study and provide consistent results. In the test results Hypothesis gives results that reject Ho and accept the hypothesis of research Ha, so that the auditor experience and audit quality together provide a significant effect on going concern opinion. Partial experience does not affect the giving of going concern opinion while the quality of audit partially affect the giving of going concern opinion.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.610
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0010.001
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 head0.054
GPT teacher head0.337
Teacher spread0.283 · 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