Roles and responsibilities when leading consensus meetings
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.252 | 0.479 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.020 | 0.016 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.024 | 0.017 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".