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Record W2552820166 · doi:10.1186/s41073-016-0024-5

Ranking major and minor research misbehaviors: results from a survey among participants of four World Conferences on Research Integrity

2016· article· en· W2552820166 on OpenAlexfundno aff
L.M. Bouter, Joeri K. Tijdink, N. H. AXELSEN, Brian C. Martinson, Gerben ter Riet

Bibliographic record

VenueResearch Integrity and Peer Review · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
FundersWomen's College Research Institute
KeywordsMinor (academic)MisconductPsychologyQuality (philosophy)Product (mathematics)Social psychologyPublic relationsPolitical scienceLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Codes of conduct mainly focus on research misconduct that takes the form of fabrication, falsification, and plagiarism. However, at the aggregate level, lesser forms of research misbehavior may be more important due to their much higher prevalence. Little is known about what the most frequent research misbehaviors are and what their impact is if they occur. METHODS: A survey was conducted among 1353 attendees of international research integrity conferences. They were asked to score 60 research misbehaviors according to their views on and perceptions of the frequency of occurrence, preventability, impact on truth (validity), and impact on trust between scientists on 5-point scales. We expressed the aggregate level impact as the product of frequency scores and truth, trust and preventability scores, respectively. We ranked misbehaviors based on mean scores. Additionally, relevant demographic and professional background information was collected from participants. RESULTS: Response was 17% of those who were sent the invitational email and 33% of those who opened it. The rankings suggest that selective reporting, selective citing, and flaws in quality assurance and mentoring are viewed as the major problems of modern research. The "deadly sins" of fabrication and falsification ranked highest on the impact on truth but low to moderate on aggregate level impact on truth, due to their low estimated frequency. Plagiarism is thought to be common but to have little impact on truth although it ranked high on aggregate level impact on trust. CONCLUSIONS: We designed a comprehensive list of 60 major and minor research misbehaviors. Our respondents were much more concerned over sloppy science than about scientific fraud (FFP). In the fostering of responsible conduct of research, we recommend to develop interventions that actively discourage the high ranking misbehaviors from our study.

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)0.037
metaresearch head score (Gemma)0.095
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.998
Threshold uncertainty score0.198

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.095
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0020.002
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 head0.672
GPT teacher head0.554
Teacher spread0.118 · 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 designObservational
DomainEvaluation
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

Citations219
Published2016
Admission routes1
Has abstractyes

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