An examination of ‘institutional ascription’: Capture of the gatekeepers of accounting veracity
Bibliographic record
Abstract
This paper aims to apply the theory of gatekeeping — institutional ascription — using the financial crisis of 2008–2009 in Iceland as a case. An investigation of the theory was conducted (Gabbioneta et al., 2014). The research question tested is whether the auditors, regulators, rating agencies, and analysts failed in the duty of stewardship to assess the scale and scope of accounting scandals and fraud perpetrated by executives of financial institutions. The paper shows that unless legal cases are prosecuted, where a complete presentation of evidence is presented, the theory has explanatory power but little predictive power, as all information must be in the public domain. The data applied in this paper is enriched by several unique elements of the situation described: a Special Investigation Commission (SIC, 2010), a report by a well-known regulator, the Office of a Special Prosecutor, (Jännäri, 2009) the role of the Supreme Court in reviewing all cases emanating from the crash, and a Report on Financial Stability (Central Bank of Iceland, 2010). Because of the extensive database provided by a combination of disinfectant and sunlight, this paper permits a richness of data across all financial institutions and an investigation of the theory of institutional ascription. The paper teaches authorities the need for more active use of the criminal system to prosecute wrongdoing.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".