Workplace Deviance Investigations: A Case Study of the Application of Maturity Model to a University Investigation
Bibliographic record
Abstract
This article presents a case study from Norway that supplements previous research in other jurisdictions such as Australia, Canada, the Netherlands, and the United Kingdom regarding lack of justice when corporate investigators conduct internal examinations in client organizations. The case is concerned with a university researcher who was investigated after allegations of violating the national working environment act. Investigators applied likelihood of fifty percent rather than the criteria of incident beyond any reasonable doubt. There was no real contradiction offered, and many more deviance from a fair process occurred when compared to the public criminal justice system. The presented maturity model with four stages is applied to illustrate the low level of investigative performance in the case. This research does not in any way claim that the presented case is representative of work by corporate investigators conducting internal examinations in client organizations. Nevertheless, this research is important, as it illustrates the lack of justice that is caused by the absence of regulation of the private investigation industry as performed by law firms, audit firms, consulting firms, and others.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 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".