Corporate financial disclosures and the importance of readability
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper draws on prior studies on the readability of corporate financial disclosures to discuss why readability should be a concern for firms. Guidance and recommendations are offered to help firms improve their financial disclosures. Design/methodology/approach The authors base their analysis on the management and accounting literature on readability. Findings This paper presents the main causes and consequences of complexity in corporate disclosures and identifies four disclosure writing styles: obfuscation, informativeness, deception and avoidance. This paper suggests that firms concerned about the readability of their communications use a balanced strategy and proposes some practical actions for its implementation. Originality/value This paper makes several contributions by offering insights into questions that should be raised by top management and the board of directors, including: Why care about readability? What are the causes and consequences of low readability? What strategies can we adopt and how should we implement them?
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it