“Transparency” in Accounting and Corporate Governance: Making Sense of Multiple Meanings
Bibliographic record
Abstract
ABSTRACT Calls for greater transparency of accounting and financial information in the aftermath of Enron and other accounting scandals appeared to offer the opportunity for greater public accountability within financial reporting. Our analysis however suggests that different emergent meanings from various groups such as senior managers, investors, regulators, and other gatekeepers were associated with these calls, indicating an underlying “taken for grantedness” concerning the need for increased transparency. We examine these emergent meanings of transparency and their effects on broader issues of accountability within financial reporting. Our analysis, employing sensemaking, shows the mobilization of “transparency” to construct meanings to make sense of and rationalize complex, ambiguous, and uncertainty events around the accounting scandals and the subsequent financial crises. This meaning construction permitted action that “restored” confidence in financial markets and in doing so served the interests of senior managers who defined their own public accountability. Our analysis also suggests that accountants and regulators need to provide higher forms of sensegiving around such crises to offer alternatives to senior managers' accounts of transparency and financial reporting.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.039 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".