Evaluating Internet Based Financial Reporting Index on the Website of Indonesian Insurance Company
Bibliographic record
Abstract
With the development of internet technologies, communication via the Internet has been adopted by the company as an important tool to provide information. The development of information technology, especially the Internet has affected the traditional form of presenting and preparing the financial statements or on the so-called Internet Financial Reporting (IFR). The efforts of the companies are for reducing information asymmetry by utilizing company's website to disclose information related to the company. This research aimed to determine the relationship of Internet Financial Reporting Index to the level of popularity of a website based on the Indonesian insurance company. The data used in this study are life insurance and general insurance companies by the number of samples of 109 companies in 2013. Spearman correlation test is used to determine the relationship IFRI with popularity website, the size of company’s website with popularity website and IFRI with company’s assets. While the Two independent sample test is used to determine differences IFRI between life insurance and general insurance sectors. The results of this study indicate that there is a significant relationship between IFRI and popularity website, the size of company’s website with popularity website and IFRI with company’s assets.. And there is a significant difference IFRI between life insurance and general insurance companies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".