ANALISIS WINDOW DRESSING PADA PERUSAHAAN BADAN USAHA MILIK NEGARA YANG TERDAFTAR DI BURSA EFEK INDONESIA PERIODE 2012-2014
Bibliographic record
Abstract
ABSTRACT This study aimed to analyze whether the practice of window dressing on the company’s state-owned enterprises listed on the Indonesia Stock Exchange. The sample was 10 companies that already publish quarterly financial reports from 2012-2014. The method of analysis in this study using t-test analysis to see if there is a difference between Q1 and Q4, between Q2 and Q4, Q3 and Q4 as well as see the movement of the cash holding in each quarter. The results showed that there is a difference between Q3 and Q4 and increased cash holdings in each quarter 4. It can happen because companies tend to raise cash holding fourth quarter financial statements to reflect the end of a nice and cash holding can be used as an instrument to give signal that a company’s balance sheet is healthy and strong. Keyword : Window Dressing, Cash Holding, Quarterly Report
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".