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Record W6944010102 · doi:10.17605/osf.io/axmjz

Roles and responsibilities when leading consensus meetings

2023· other· en· W6944010102 on OpenAlexaff

Bibliographic record

VenueOpen Science Framework · 2023
Typeother
Languageen
FieldDecision Sciences
Topicscientometrics and bibliometrics research
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPeer reviewProcess (computing)Grey literatureInclusion (mineral)CitationMEDLINE

Abstract

fetched live from OpenAlex

Objective: To map the roles, responsibilities, criteria for evaluating performance, and characteristics of effective leadership among chairs and other principals of meetings where the goal is to reach a consensus-based decision. Introduction: The processes of peers evaluating grant applications to allocate research funding is considered essential to the discourse of science 2 3. Yet the process is increasingly questioned, and prone to bias. Furthermore, the structure and interactions of peer review committees is not always equitable. Inclusion criteria: Studies that report on leadership roles within consensus decision committees that follow a similar process to the grant peer review process, across organizations that are tied to funding directives. This review will include studies from a range of disciplines; health science, biomedicine, education, psychology, management research, law, ethics and policy. Methods: With the help of a medical librarian, we will create comprehensive search strategies using a range of bibliographic databases, citation indexes and websites. We will search the websites of academic bodies (e.g., learned societies) and other web sources of information (e.g., management research organizations) to locate grey papers. Two independent reviewers will screen abstracts and subsequently full-text articles, to identify potentially eligible studies for inclusion. This scoping review will report on the roles, responsibilities and potential performance indicators of consensus decision committees that follow a similar process to grant peer review.

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 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)0.073
metaresearch head score (Gemma)0.297
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Bibliometrics, Science and technology studies, Scholarly communication, Open science, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch, Bibliometrics, Open science, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.224
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0730.297
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0670.155
Science and technology studies0.0010.004
Scholarly communication0.0210.001
Open science0.0150.010
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.527
GPT teacher head0.596
Teacher spread0.069 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2023
Admission routes1
Has abstractyes

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