PENGARUH UKURAN PERUSAHAAN, PROFITABILITAS, \nREPUTASI KAP DAN AUDITOR SPESIALIS TERHADAP \nPEMBERIAN OPINI AUDIT GOING CONCERN PADA \nPERUSAHAAN YANG MELAKUKAN INITIAL PUBLIC \nOFFERING
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh ukuran perusahaan, \nprofitabilitas, reputasi KAP dan auditor spesialis terhadap pemberian opini audit \ngoing concern pada perusahaan yang melakukan Initial Public Offering (IPO). \nPenelitian ini berfokus hanya pada ukuran perusahaan IPO dan profitabilitas \n(ROA) untuk aspek finansial serta reputasi KAP dan auditor spesialis sebagai \naspek non-finansial. Seluruh perusahaan IPO yang terdaftar di BEI merupakan \npopulasi dalam penelitian ini. Dalam kurun waktu 2007-2015, 163 perusahaan \ndiambil sebagai sampel. Pengumpulan data diperoleh dari BEI. Analisis regresi \nlogistik digunakan untuk menguji hipoesis. Hasil analisis menunjukkan bahwa \nukuran perusahaan, profitabilitas (ROA), reputasi KAP dan auditor spesialis tidak \nberpengaruh terhadap pemberian opini audit going concern pada perusahaan yang \nmelakukan IPO. Opini audit going concern yang diterima perusahaan IPO dapat \nmembantu para manajer dan juga investor untuk mengurangi ex ante uncertainty \n(ketidakpastian yang belum terjadi) pada saat perusahaan memasuki pasar \nsekunder sampai dengan dua tahun setelah tahun IPO.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.067 | 0.012 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".