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Record W1999950133 · doi:10.1172/jci24343

The great betrayal Fraud in science

2005· article· en· W1999950133 on OpenAlexaboutno aff
Alan R. Price

Bibliographic record

VenueJournal of Clinical Investigation · 2005
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
Fundersnot available
KeywordsBetrayalPolitical scienceLaw

Abstract

fetched live from OpenAlex

Horace Judson’s new historical commentary, The great betrayal: fraud in science, is a stimulating and thoughtful book for readers interested in science, publication ethics, and research misconduct. He writes with his typical flair, drawing on interviews with famous scientists. His premise is that scrutiny of the nature of fraud and other misconduct will reach to the pulse of what the sciences are and what scientists do. The book offers little, however, for readers whose principal interest is clinical research. Judson is a respected modern historian of science, perhaps best known for his history of molecular biology, The eighth day of creation: makers of the revolution in biology (S1). Most of the citations in this new book precede 1996, except for references to stories of misconduct among businessmen, historians, and journalists that have filled the press since 2000. In fact, the 35 “Recent Cases” are almost the same set of cases (late 1800s-late 1900s) previously described in Betrayers of the truth (S2) and False prophets (S3), neither of which is referenced in this new book. The newest cases cited in Judson’s book involve major scandals in a German and an American laboratory, heavily covered in the recent science press. Nonetheless, Judson does profitably draw on the classical cases noted in these earlier books, drawing analogies with cases of scientific fraud during the past decade. Also missing is a discussion of the findings of scientific misconduct in clinical cases made between 1992 and 2004 by the Office of Research Integrity (ORI), part of the United States Public Health Service (PHS) in the Department of Health and Human Services (HHS), which would have been of interest to JCI readers. About 40 of the 150 findings have involved clinical or related nonclinical research with human subjects (S4, S5). The only such case described by Judson is a clinical trial on the use of mastectomy versus lumpectomy to treat breast cancer, conducted at St. Luc’s Hospital, Montreal, and coordinated by the University of Pittsburgh, in which the investigator falsified records to make more patients eligible for the study. However, Judson includes a long, solid chapter on “The Baltimore Affair,” dealing with the allegations of falsification of data on the part of a Massachusetts Institute of Technology scientist and the overturning of the ORI’s 1994 finding against her in 1996 by the HHS appeals board. This case had been discussed in detail in 2 previous books, Science on trial: the whistle blower, the accused, and the Nobel laureate (S6), and The Baltimore case: a trial of politics, science, and character (S7), which also are not cited in Judson’s new book. To his credit, Judson personally reviewed records and interviewed witnesses, describing both in much the same style as he used in The eighth day of creation (S1), which makes the new book a fascinating read. Also striking is Judson’s initial chapter, “A Culture of Fraud,” describing public cases of fraud by businessmen, social scientists, clergy, and others. The conclusion is obvious: a few scientists are likely no better and no worse than the few members of the general population who are crooks and charlatans. His epilogue includes a plea to restore high standards to the conduct of scientific research. Several interesting chapters, titled “The problems of peer review,” “Authorship, ownership: problems of credit, plagiarism, and intellectual property,” and “Laboratory to law: the problems of institutions when misconduct is charged” will be enlightening and challenging to readers who deal with such problems and try to teach students how to avoid them in the responsible conduct of research. Judson examines self-governance in science and whether officials, editors, and peers can deal effectively, without conflicts of interest, with deviations from appropriate standards in conducting, reporting, and reviewing research. He focuses as well on the important role of whistle-blowers or complainants in challenging some reports for reasons of integrity. Judson’s book is accurate and timely, in that in 2000, the US Office of Science and Technology developed a uniform policy for dealing with allegations and investigations for all federal agencies. Each agency has since been publishing new policies or regulations; the revised HHS regulation should soon be finalized. In response, universities, research hospitals, and institutes are updating their policies and procedures, at least to address the federal government’s new definition: “Research misconduct is defined as fabrication, falsification, or plagiarism in proposing, performing, or reviewing research, or in reporting research results . . . [A] finding . . . requires that: there be a significant departure from accepted practices of the relevant research community; and the misconduct be committed intentionally, or knowingly, or recklessly; and the allegation be proven by a preponderance of evidence.” Judson has provided a nice history of the public debate over this definition through the 1990s and the creation of the ORI. His book accomplishes its goal, to describe and analyze the history of fraud in science and its impact on the scientific and public communities, and he examines what contributes to fraud. The book is recommended to readers interested in fundamental research ethics and their historical and political context in the new millennium. This review represents the personal views of the author and not necessarily any position of the ORI, PHS, or HHS. References are available online with this article; doi:10.1172/JCI200524343DS1.

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 imitation

Not 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.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.027
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.993
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.027
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0100.027
Scholarly communication0.0140.012
Open science0.0010.004
Research integrity0.0070.010
Insufficient payload (model declined to judge)0.0030.001

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 head0.600
GPT teacher head0.636
Teacher spread0.036 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainMethods
GenreCommentary

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

Citations9
Published2005
Admission routes1
Has abstractyes

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