Beyond the boardroom: governmental perspectives on corporate governance
Bibliographic record
Abstract
Purpose This paper aims to examine corporate governance and consequences of the Sarbanes‐Oxley Act (SOX) in the US from a socio‐political perspective. Design/methodology/approach The author employs neo‐liberalism and its related mentality of governmentality to develop an analysis of how corporate governance and reforms such as SOX are socially constructed through autonomous agents, including managers and accountants, and various power relationships that comprise government. Findings This paper theorizes that legislative reform, such as SOX, represents pervasive mechanisms of disclosure, surveillance and power, and an insurance rationality designed to manage the new and significant risks of corporate governance. A framework is established which conceptualizes SOX as the intersection of neo‐liberalism, political rationalities and governmental techniques, and accounting practices which lead to the elements of security, quantification and shareholder value. Through this framework a model of risk as governance is developed that examines SOX through technologies of the self, calculation and insurance, designed to act upon managers using knowledge about control or financial statement weaknesses. Such mechanisms identify corporate governance risks, which can be acted upon by outside experts, such as accountants. Originality/value The major inference from this paper is that corporate governance research in accounting should pursue new lines of inquiry, which will permit the more profitable extension of existing research. Such inquiry should focus less on empirical corporate governance factors and more on the relationships, and power constructs of corporate governance, as well as how legislative reforms employ tactics to normalize the behaviour of not only managers, but also accountants.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".