Bibliographic record
Abstract
This case study looks at how a fraudulent PhD thesis from a Canadian university became entangled in US politics, attracting US and Canadian media attention. In 2013, Douglas Mastriano, a US Army colonel, was granted a History PhD from the University of New Brunswick (UNB) under unusual circumstances. A thesis committee member was removed without his consent, then informed by UNB that the thesis had been approved over his strong objections. UNB declared a 17-year-long embargo on public access to Mastriano’s thesis (it later admitted that its rules limit thesis embargoes to 4 years). In 2014, Mastriano published a book based on his thesis. Respected historians found multiple instances of academic fraud in it, including falsification of archival information. Scholars who requested access to Mastriano’s thesis were rebuffed by UNB. There matters might have rested. However, in November 2022 Douglas Mastriano ran for Governor of Pennsylvania. A Donald Trump supporter and participant in the Jan. 6, 2021 US Capitol invasion, he often cited his PhD from UNB during his campaign. Under media pressure, UNB released his thesis in October 2022. So far, 213 cases of serious academic fraud have been found in it. Before November 2022, UNB promised that “two independent investigators” would investigate Mastriano’s 2013 thesis and the 17-year-embargo on access to it. Subsequently, UNB has walked back that promise. Attendees will learn about the practical and ethical issues faced by a Canadian university when an old case of academic fraud unexpectedly becomes a source of controversy in the media.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.009 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".