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Record W3135000317 · doi:10.1186/s41073-021-00108-4

Steps toward preregistration of research on research integrity

2021· article· en· W3135000317 on OpenAlexfundno aff
Klaas Sijtsma, Wilco H. M. Emons, Nicholas H. Steneck, L.M. Bouter

Bibliographic record

VenueResearch Integrity and Peer Review · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicAcademic integrity and plagiarism
Canadian institutionsnot available
FundersWomen's College Research Institute
KeywordsResearch integrityUSablePsychologyData integritySet (abstract data type)DocumentationData collectionQualitative researchComputer scienceData sciencePublic relationsSociologyPolitical scienceWorld Wide WebDatabaseSocial science

Abstract

fetched live from OpenAlex

BACKGROUND: A proposal to encourage the preregistration of research on research integrity was developed and adopted as the Amsterdam Agenda at the 5th World Conference on Research Integrity (Amsterdam, 2017). This paper reports on the degree to which abstracts of the 6th World Conference in Research Integrity (Hong Kong, 2019) reported on preregistered research. METHODS: Conference registration data on participants presenting a paper or a poster at 6th WCRI were made available to the research team. Because the data set was too small for inferential statistics this report is limited to a basic description of results and some recommendations that should be considered when taking further steps to improve preregistration. RESULTS: 19% of the 308 presenters preregistered their research. Of the 56 usable cases, less than half provided information on the six key elements of the Amsterdam Agenda. Others provided information that invalidated their data, such as an uninformative URL. There was no discernable difference between qualitative and quantitative research. CONCLUSIONS: Some presenters at the WCRI have preregistered their research on research integrity, but further steps are needed to increase frequency and completeness of preregistration. One approach to increase preregistration would be to make it a requirement for research presented at the World Conferences on Research Integrity.

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
gemmaMetaresearchResearch integrity
Domain: Methods · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearchResearch integrityOpen science
Domain: Reproducibility · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.859
metaresearch head score (Gemma)0.866
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.968
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.8590.866
Meta-epidemiology (narrow)0.0030.008
Meta-epidemiology (broad)0.0070.010
Bibliometrics0.0310.025
Science and technology studies0.0200.024
Scholarly communication0.0390.047
Open science0.0210.039
Research integrity0.0320.061
Insufficient payload (model declined to judge)0.0140.018

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 head0.614
GPT teacher head0.598
Teacher spread0.016 · 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.

MetaresearchResearch integrityOpen science

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

Study designTheoretical or conceptual
DomainMethods · Reproducibility
GenreMethods · Commentary

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

Citations6
Published2021
Admission routes1
Has abstractyes

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