The presentation of graphs in annual reports: The case of the KLSE corporate awards winners
Bibliographic record
Abstract
Misleading graphs could be the result of annual report preparers’ ignorance, carelessness, or intention to use impression management. The graphs impair the communication effectiveness, even though this has not been tested empirically.Unfortunately, the use of the graphs appears to be widespread due to many parties (for example users, auditors, and preparers of annual reports) not familiar with the potential abuses of construction standards.The aim of this study is to determine whether annual reports by the Kuala Lumpur Stock Exchange (KLSE, now known as Bursa Malaysia) Corporate Awards winners contain inconsistent graphs by assessing graph accuracy based on the guidelines for good graphics as set forth in previous literature and graph discrepancy index (GDI), also known as graph measurement distortion (part of impression management).The methodology used is an adaptation of the graphical guidelines developed by Schmid and Schmid (1979), Tufte (1983), Jarett and Babad (1988), Canadian Institute of Certified Accountant (CICA) (1993), Jarett (1993), and subsequently applied by Frownfelter-Lohrke and Fulkerson (2001).The GDI was calculated using a variant of Tufte’s (1983) lie factor.As far as we are aware, this is the first study in Malaysia to investigate the graph presentation in annual reports of companies that had received awards for their annual reports.It was discovered that 95% of companies in our sample used graphs, which included most of the key financial variables (KFVs) graphs, that exhibited more material overstatement (mean +61.78%) than understatement distortion (mean -45.79%), and many graphs conform to the suggested graph guidelines.
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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 | 0.013 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".