Rethinking Crime, Harm, and Corporate Responsibility: Lessons from the Post Office Scandal
Bibliographic record
Abstract
The Post Office Scandal is recognised as one of the most significant miscarriages of justice in British legal history. Using a conceptual review grounded in Zemiological theory this article explores the scandal, arguing that traditional frameworks of criminology fail to capture the full scope of corporate crime. The article begins by tracing the origins of the scandal to the flawed implementation of the Horizon IT system, examining how institutional failures by Fujitsu, the Post Office and the UK Government enabled two decades of systemic injustice. It then applies the theory of Zemiology to challenge dominant constructions of crime, highlighting how the pursuit of profit and poor corporate governance permitted a range of ontological harms to sub-postmasters. The role that inadequate safeguarding in private prosecutions played in the scandal is then considered, drawing comparison to the practices of the RSPCA. Lastly, the article considers the enduring legacy of the scandal and questions whether institutional trust can be rebuilt. The article concludes that whilst reparations and reforms have been made, the Post Office Scandal has caused irreparable damage to the integrity of trusted institutions.
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 | 0.009 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.035 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| 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".