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Record W4234553601 · doi:10.31219/osf.io/m9abx

The Hong Kong Principles for Assessing Researchers: Fostering Research Integrity

2019· preprint· en· W4234553601 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicArtificial Intelligence in Healthcare and Education
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsTransparency (behavior)TrustworthinessEngineering ethicsResearch integrityInclusion (mineral)ManifestoPolitical scienceResearch ethicsPublic relationsOpen scienceQuality (philosophy)DeclarationComputer scienceSociologyEngineeringInternet privacyLawSocial scienceEpistemology

Abstract

fetched live from OpenAlex

The primary goal of research is to advance knowledge. For that knowledge to benefit research and society, it must be trustworthy. Trustworthy research is robust, rigorous and transparent at all stages of design, execution and reporting. Initiatives such as the San Francisco Declaration on Research Assessment (DORA) and the Leiden Manifesto have led the way bringing much needed global attention to the importance of taking a considered, transparent and broad approach to assessing research quality. Since publication in 2012 the DORA principles have been signed up to by over 1500 organizations and nearly 15,000 individuals. Despite this significant progress, assessment of researchers still rarely includes considerations related to trustworthiness, rigor and transparency. We have developed the Hong Kong Principles (HKPs) as part of the 6th World Conference on Research Integrity with a specific focus on the need to drive research improvement through ensuring that researchers are explicitly recognized and rewarded (i.e., their careers are advanced) for behavior that leads to trustworthy research. The HKP have been developed with the idea that their implementation could assist in how researchers are assessed for career advancement with a view to strengthen research integrity. We present five principles: responsible research practices; transparent reporting; open science (open research); valuing a diversity of types of research; and recognizing all contributions to research and scholarly activity. For each principle we provide a rationale for its inclusion and provide examples where these principles are already being adopted.

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: Evaluation · Genre: Methods
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptuallow
gptMetaresearchResearch integrity
Domain: Evaluation · Genre: Commentary
About the Canadian research system: no · About a Canadian topic: no
Theoretical or conceptualhigh
models agreeAgreement 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)0.011
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0000.000

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 head0.905
GPT teacher head0.657
Teacher spread0.248 · 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