Corporate reporting on the internet: some implications for the auditing profession
Bibliographic record
Abstract
Purpose The exponential growth in corporate reporting on the internet has created numerous opportunities and challenges for the accounting and auditing profession, and regulators. This study aims to examine internet reporting practices of companies in Malaysia for the purpose of exploring their auditing implications. Design/methodology/approach An examination of the 100 Kuala Lumpur Stock Exchange Composite Indexed (KLSE CI) companies in Malaysia in 2003 and 2004. Findings Although there has been an increase in both the number of companies and the types of information provided on the internet, the quality of internet reporting information to users has little improved. This problem is compounded because auditors have little control over web contents and the changes that can be made to audited information. Further guidance to standardise the types of internet reporting information may help protect the interest of users, provide more certainty to what information needs to be audited and reduce audit risks. Practical implications The hosting of audited information on an auditor's web site may provide auditors with better control, reduce audit risks and further improve the credibility and reliability of information to users. Originality/value Provides information on the financial reporting and auditing challenges posed by internet reporting.
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 | 0.009 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".