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Record W1965217323 · doi:10.1136/bmj.d8357

Research misconduct in the UK

2012· letter· en· W1965217323 on OpenAlex
Fiona Godlee, Elizabeth Wager

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBMJ · 2012
Typeletter
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
Fundersnot available
KeywordsMisconductScientific misconductCriminologyPsychologyPolitical scienceLawMedicine

Abstract

fetched live from OpenAlex

Time to act Research misconduct can harm patients, distort the evidence base, misdirect research effort, waste funds, and damage public trust in science. Countries all over the developed world are now recognising the need to set up systems to deter, detect, and investigate research misconduct. Why does the United Kingdom have no plans to do the same? As Aniket Tavare outlines in the linked feature (doi:10.1136/bmj.d8212),1 high profile cases of misconduct have led the United States, Canada, Sweden, Norway, and Poland, among others, to create formal mechanisms for overseeing research integrity. In most countries responsibility lies with the institutions, but oversight varies greatly, and it is unclear which systems are most effective and efficient. None is perfect—the remit of the US Office of Research Integrity is limited to publicly funded health research; Australia’s recently established Research Integrity Committee is already being criticised for lacking teeth. But each system shows that the problem has been acknowledged, that institutions accept primary responsibility, and that governments and funders are seriously committed to tackling misconduct openly and with a range of statutory powers. In contrast, the UK has no official national body. The UK Research Integrity Office was established in 2006 and has done some useful things. But its function has always been advisory, and now that the major funders represented by Research Councils UK (RCUK) have decided not to continue the funding, it relies on voluntary funding from institutions. The Research Integrity Futures Working Group, set up by RCUK and Universities UK (UUK) and other bodies, has also apparently come to nothing. The working group’s report commissioned in 2009 called for an independent advisory body, …

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.

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearchResearch integrity
Domain: Methods · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
gptResearch integrity
Domain: not available · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Not applicablehigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.019
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.297
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0190.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.017
Insufficient payload (model declined to judge)0.0020.001

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 head0.220
GPT teacher head0.461
Teacher spread0.242 · 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