Forensic Acculturation for Accountability in Local Governments: A Design Science Approach for School Leaders and Citizens
Bibliographic record
Abstract
ABSTRACT Using design science methodology, this article describes a field-tested model of forensic accountants assisting average citizens in improving local governmental accountability and the provision of services via forensic acculturation. While most citizens lack the skills of forensic accountants, citizens have several traits that make them uniquely capable of detecting unhealthy financial behaviors. The field research uncovered systematic patterns of citizens being stymied in moving from the “being aware stage” to the “making improvements” or “holding accountable” stages. We label the patterns of being stymied as 10 Ds (delay, deflect, distort, etc.). We also discovered more successful individuals using behaviors we label the 3 Ps (polite, persistent, and professional) that are common traits among forensic experts. We provide case study evidence of the effects of citizen training and consultation processes, and refine a teaching tool (“3 Ps and 10 Ds”) for use in further field tests. JEL Classifications: D71; D72; D85; G34; G38; H70; I22; M48. Data Availability: Data are available from the author and the public sources cited in the text.
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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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".