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Record W2765187625 · doi:10.1136/bmj.j4904

Canadian researchers whose studies questioned vaccine safety face second retraction

2017· letter· en· W2765187625 on OpenAlexaboutno aff
Owen Dyer

Bibliographic record

VenueBMJ · 2017
Typeletter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsFace (sociological concept)PsychologyLibrary scienceHistoryMedicineSociologyComputer scienceSocial science

Abstract

fetched live from OpenAlex

A study that linked aluminium vaccine adjuvants to genetic changes “homologous with biomarkers of autism” in mice is to be retracted by the Journal of Inorganic Chemistry .1 The retraction comes after readers of the website PubPeer claimed to have found image alterations in the paper. The study authors Christopher Shaw and Lucija Tomljenovic of the University of British Columbia then reviewed the data and found evidence of western blot image alterations. They contacted the journal and asked for the retraction, Shaw told The BMJ . The journal’s editor, John Dawson of the University of South Carolina, told the website Retraction Watch, “The paper by Shaw and co-workers is being retracted jointly by the authors …

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

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
gemmaResearch integrity
Domain: not available · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: yes
Not applicablelow
gptResearch integrityMetaresearch
Domain: Evaluation · Genre: Editorial
About the Canadian research system: no · About a Canadian topic: yes
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)0.005
metaresearch head score (Gemma)0.171
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.218
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.171
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.006
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 head0.314
GPT teacher head0.525
Teacher spread0.211 · 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

Labeled directly by 2 models reading the full record.

Research integrityMetaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable
DomainEvaluation
GenreEditorial

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

Citations1
Published2017
Admission routes1
Has abstractyes

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