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Record W4412642329 · doi:10.6000/1929-4409.2025.14.14

Rethinking Crime, Harm, and Corporate Responsibility: Lessons from the Post Office Scandal

2025· article· en· W4412642329 on OpenAlexvenueno aff
Alisse Drew-Griffiths

Bibliographic record

VenueInternational Journal of Criminology and Sociology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicRegulation and Compliance Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHarmCriminologyBusinessLawPolitical scienceSociology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.483
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.111
GPT teacher head0.336
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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