Recent advances in internal control: Soft control overcoming the limits of hard control
Bibliographic record
Abstract
Uncertainties and risks continue to pose a threat to governance and internal control, impeding public sector modernization and essential service delivery. Attempts to develop alternative strategies to meet desired results in highly bureaucratic institutional environments such as the public sector are intensifying because ideas and principles matter. This study advances informal "soft control" as a substitute for formal "hard control" in four dimensions: (a) creates a clear difference between "soft" and "hard" control; (b) designs key determinants of informal "soft" control; (c) limitations of formal "hard" control; and (d) a conceptual framework and hypotheses to support future empirical research and “operationalization” of the proposed constructs. Critical observations imply that the increasing cases of corporate malpractice and consequential non-alignments with best practises in recent times are sufficient evidence to suggest that formal control is incapable of mitigating financial crimes, irregularities, and preventing complex accounting scandals classified as white-collar fraud. The causes of these control failures are attributed to overreliance on "hard control" which primarily works with sanctions and the neglect of informal control mechanisms "soft controls". This condition has limited the ability of auditors to uncover systematic failures of controls that are process-specific, resulting in a partial and incomplete evaluation of internal controls. The study assumed a theoretical approach due to the lack of existing empirical research on “soft control”. However, this observations form a solid theoretical foundation for further discussions. We argue that "tone at the top," informal social control, organisational culture, ethical values, empowerment, and employee competence are effective substitutes for and complements to formal "hard" controls in preventing another Enron.
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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.016 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".