Bad apples, bad barrels and bad cellars: a “boundaries” perspective on professional misconduct
Bibliographic record
Abstract
Parmalat – formerly one of the largest dairy companies in the world – went bankrupt on December 24, 2003, leaving behind a “hole” of 14 billion euros, a sum almost twice the company's 2002 sales turnover. Subsequent investigations revealed that the company's financial accounts had consistently and deliberately been falsified for each of the thirteen years that Parmalat was listed on the Milan Stock Exchange. During those years, professionals – who are supposed to act as gatekeepers (Coffee 2005, 2006) – did not (or, in some cases, decided not to) see what was going on. Deloitte – which was auditing the company's group accounts – did not raise any concerns until October 2003 when they issued a disclaimer on the company's accounts, as they could not determine the amount of a fund held by a subsidiary. Standard and Poor's constantly rated the company at the investment grade and even improved its outlook only a few months before the company defaulted. Securities analysts, similarly, remained positive on Parmalat's shares until the company was already on the verge of bankruptcy; only a few days before the fraud was detected, fifty-seven of sixty-six of their equity research reports recommended buying or holding the company's shares. Enron's story, one of the biggest scandals in American history, is not much different, although it took place in the supposedly more developed US stock market. As in the Parmalat story, professionals did not effectively perform their ascribed roles. Arthur Andersen, the company's auditors, consistently approved Enron's accounts without raising any concerns and failed to inform “the company's audit committee about both the accounting policies in use at Enron and the unusual transactions the company had conducted” (Batson Report 2003: 40–41). Management consultancies such as McKinsey, which advised Enron, endorsed its strategic repositioning and praised its “asset light” strategy (Kipping, Kirkpatrick, and Muzio 2006). Not until four days before bankruptcy was declared on December 2, 2001, did rating agencies lower their ratings of the company to below the mark of a safe investment. As late as October in the same year, fifteen securities analysts rated Enron a “buy” and twelve of them recommended it as a “strong buy.”
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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 | 0.003 | 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 | 0.004 | 0.027 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".