Sources and Evidence Notes

Research cut-off: 2026-07-23. All links were checked on that date with Parallel web search and focused extraction. Dates below are publication, release, or page-update dates shown by the source. Source excerpts were treated as untrusted text and checked against the canonical page.

This bibliography supports process guidance; it does not show that any single brainstorming method universally improves creativity or scientific validity.

Group brainstorming, blocking, fixation, and selection

Diehl and Stroebe (1987) — primary experiments

Michael Diehl and Wolfgang Stroebe, “Productivity loss in brainstorming groups: Toward the solution of a riddle,” Journal of Personality and Social Psychology 53(3), 497–509. DOI 10.1037/0022-3514.53.3.497. Published 1987.

Four experiments examined free riding, evaluation apprehension, and production blocking.
Direct manipulation in the fourth experiment supported production blocking as an important explanation for loss in the tested interacting groups.
Scope limit: laboratory brainstorming tasks do not establish superiority for every group purpose or setting.

Mullen, Johnson, and Salas (1991) — meta-analysis

Brian Mullen, Craig Johnson, and Eduardo Salas, “Productivity loss in brainstorming groups: A meta-analytic integration,” Basic and Applied Social Psychology 12(1), 3–23. DOI 10.1207/s15324834basp1201_1. Published 1991.

The reviewed studies generally favored nominal over interacting groups for idea quantity and rated quality.
Loss varied with conditions such as group size and vocal versus written contribution.
Historical tasks, methods, and outcome definitions limit direct transfer to modern scientific teams.

Rietzschel, Nijstad, and Stroebe (2006) — primary experiment

Eric F. Rietzschel, Bernard A. Nijstad, and Wolfgang Stroebe, “Productivity is not enough: A comparison of interactive and nominal brainstorming groups on idea generation and selection,” Journal of Experimental Social Psychology 42(2), 244–251. DOI 10.1016/j.jesp.2005.04.005. Published March 2006.

In 42 analyzed three-person student groups, nominal groups generated more ideas; their ideas were more original and less feasible.
Selected-idea quality did not differ among conditions, and selection was not significantly better than chance.
Strictly separating generation and selection did not establish a universal selection advantage in this experiment.

Rietzschel, Nijstad, and Stroebe (2010) — primary experiments

Eric F. Rietzschel, Bernard A. Nijstad, and Wolfgang Stroebe, “The selection of creative ideas after individual idea generation: Choosing between creativity and impact,” British Journal of Psychology 101(1), 47–68. DOI 10.1348/000712609X414204. Published February 2010.

Explicit selection criteria improved selection on the named originality dimension in the reported studies, with trade-offs in satisfaction or rated effectiveness.
This supports explicit criteria, not a claim that a single composite score identifies the scientifically best idea.

Smith, Ward, and Schumacher (1993) — primary experiments

Steven M. Smith, Thomas B. Ward, and Jay S. Schumacher, “Constraining effects of examples in a creative generation task,” Memory & Cognition 21, 837–845. DOI 10.3758/BF03202751. Published November 1993.

Across three creative-generation experiments, participants exposed to examples were more likely to reproduce example features.
The specific drawing/design tasks support an anchoring or fixation risk; they do not quantify the effect for scientific ideation.

Esser (1998) — review

James K. Esser, “Alive and Well after 25 Years: A Review of Groupthink Research,” Organizational Behavior and Human Decision Processes 73(2–3), 116–141. DOI 10.1006/obhd.1998.2758. Published February 1998.

Reviews the evidence and evolution of Janis's groupthink model.
The broader literature includes substantial debate over the model and its antecedents; this skill therefore uses “groupthink” as a risk prompt, not a definitive diagnosis.

Nominal groups, Delphi, and structured elicitation

Van de Ven and Delbecq (1972) — early NGT paper

Andrew H. Van de Ven and André L. Delbecq, “The nominal group as a research instrument for exploratory health studies,” American Journal of Public Health 62(3), 337–342. DOI 10.2105/AJPH.62.3.337. Published March 1972.

Primary early account of the nominal-group approach in exploratory health research.
Historical evidence should be combined with current, context-specific reporting guidance.

Harb et al. (2021) — NGT scoping review

Sami I. Harb et al., “Methodological options of the nominal group technique for survey item elicitation in health research: A scoping review,” Journal of Clinical Epidemiology 139, 140–148. DOI 10.1016/j.jclinepi.2021.08.008. Published November 2021.

Included 57 studies and identified 30 process decision points across five broad stages.
Supports documenting actual choices and their rationale rather than treating NGT as one invariant protocol.
Scope is health-survey item elicitation, not every use of NGT.

Jünger et al. (2017) — CREDES systematic review and guidance

Saskia Jünger et al., “Guidance on Conducting and REporting DElphi Studies (CREDES) in palliative care,” Palliative Medicine 31(8), 684–706. DOI 10.1177/0269216317690685. E-published 13 February 2017.

A methodological systematic review of 30 palliative-care Delphi studies found substantial variation in conduct, terminology, and reporting.
CREDES is reporting and conduct guidance developed in a palliative-care context, not proof that Delphi consensus is correct.

RAND (2023) — official Delphi methods guidance

Dmitry Khodyakov, Sean Grant, Jack Kroger, and Melissa Bauman, RAND Methodological Guidance for Conducting and Critically Appraising Delphi Panels. RAND Corporation, 29 December 2023. DOI 10.7249/TLA3082-1.

Defines Delphi as iterative, anonymous, structured group communication under uncertainty and provides a design/appraisal tool.
RAND explicitly notes a lack of consistent methodological guidance across the many Delphi variants.

EFSA (2014) — official expert-knowledge elicitation guidance

European Food Safety Authority, “Guidance on Expert Knowledge Elicitation in Food and Feed Safety Risk Assessment,” EFSA Journal 12(6):3734. DOI 10.2903/j.efsa.2014.3734. Adopted 22 May and published 19 June 2014.

Covers problem definition, preparation, expert/method selection, elicitation, uncertainty, aggregation, and documentation.
Developed for EFSA food/feed risk assessment; apply domain-specific guidance elsewhere.

Cooke, Mendel, and Thijs (1988) — structured judgment method

Roger Cooke, Max Mendel, and Wim Thijs, “Calibration and information in expert resolution; a classical approach,” Automatica 24(1), 87–94. DOI 10.1016/0005-1098(88)90011-8. Published 1988.

Presents calibration and information concepts for combining expert judgments and an experiment with descriptive value.
Using a “calibration” label without the method's elicitation, seed questions, and validation requirements would be misleading.

Rigor, reproducibility, and sex as a biological variable

NIH rigor and reproducibility guidance

NIH Office of Extramural Research, Guidance: Rigor and Reproducibility in Grant Applications. Page dated 16 October 2024.

Identifies rigor of prior research, rigorous experimental design, relevant biological variables, and authentication of key resources.
This is grant guidance. It does not validate an idea or substitute for a protocol, statistical review, or field-specific reporting standard.

NIH SABV policy and current ORWH guidance

NIH Office of Research on Women's Health, Sex as a Biological Variable. Page last updated 17 October 2025.

NIH expects sex to be considered in design, analysis, and reporting for vertebrate animal and human studies and requests strong justification for a single-sex scope.
The page links the governing NOT-OD-15-102, released 9 June 2015 and effective for relevant applications from 2016.
Scope is NIH policy; other funders and jurisdictions may differ.

Preregistration and open science

Nosek et al. (2018) — preregistration perspective

Brian A. Nosek et al., “The preregistration revolution,” Proceedings of the National Academy of Sciences 115(11), 2600–2606. DOI 10.1073/pnas.1708274114. Published 13 March 2018.

Frames preregistration as a way to distinguish prediction from postdiction by specifying questions and analyses before observing outcomes.
It is a methodological perspective, not evidence that registration alone guarantees rigor or reproducibility.

Nosek et al. (2015) — TOP Guidelines

Brian A. Nosek et al., “Promoting an open research culture,” Science 348(6242), 1422–1425. DOI 10.1126/science.aab2374. Published 26 June 2015.

Proposes modular journal-policy standards for transparency and openness.
A policy framework does not itself establish that a specific study is valid or ethically shareable.

COS/OSF — current operational guidance

Center for Open Science, Preregistration and Preregistration: A Plan, Not a Prison. Current pages accessed 23 July 2026.

Explicitly values both exploratory and confirmatory research, recommends transparent labeling, and explains how to report deviations.

Center for Open Science, Registered Reports. Current page accessed 23 July 2026.

Describes Stage 1 protocol review and in-principle acceptance before results, with exploratory analyses reported separately.
COS states that Registered Reports are not a panacea for every field or design.

Responsible AI and research integrity

Doshi and Hauser (2024) — primary randomized experiment

Anil R. Doshi and Oliver P. Hauser, “Generative AI enhances individual creativity but reduces the collective diversity of novel content,” Science Advances 10(28):eadn5290. DOI 10.1126/sciadv.adn5290. Published 12 July 2024.

Preregistered online experiment: 293 analyzed UK participants wrote eight-sentence stories with no, one, or up to five GPT-4 ideas.
AI access improved average evaluated novelty/usefulness, especially for lower-scoring writers, while outputs became more similar in aggregate.
Constrained short-story writing is not scientific ideation; the study supports a risk hypothesis, not a universal creativity claim.

Walters and Wilder (2023) — primary citation audit

William H. Walters and Esther Isabelle Wilder, “Fabrication and errors in the bibliographic citations generated by ChatGPT,” Scientific Reports 13. DOI 10.1038/s41598-023-41032-5. Published 7 September 2023.

Audited 84 texts produced in April 2023 by tested GPT-3.5 and GPT-4 versions and documented fabricated and substantive citation errors.
Results are model-, prompt-, task-, and date-specific; they justify direct source verification rather than a timeless error rate.

European Commission / ERA Forum (2024) — official living guidance

European Commission Directorate-General for Research and Innovation, Guidelines on the responsible use of generative AI in research. Published 20 March 2024.

Emphasizes transparency, responsibility, privacy, confidentiality, intellectual property, bias awareness, and avoiding AI in sensitive evaluation activities when confidentiality is not assured.
The Commission describes the guidance as living and subject to updates.

UNESCO (2023; updated 2026) — official guidance

Fengchun Miao and Wayne Holmes, Guidance for generative AI in education and research. Published 7 September 2023; page last updated 16 January 2026.

Uses a human-centered framing and addresses privacy, ethical validation, inclusion, equity, and institutional capacity.
Much of the document concerns education; apply research-specific provisions with local policy.

ICMJE (2026) — official publishing recommendations

International Committee of Medical Journal Editors, Use of Artificial Intelligence in Publishing. Recommendations page dated 2026.

Humans remain responsible for accuracy, attribution, permissions, confidentiality, and disclosure; AI tools are not authors.
Publishing guidance does not replace institutional research policy.

ALLEA (2023) — research-integrity code

ALLEA, The European Code of Conduct for Research Integrity, 2023 Revised Edition. DOI 10.26356/ECOC. Published 2023.

Cross-disciplinary self-regulatory framework recognized as a reference for EU-funded research.
General principles require translation into institutional and discipline-specific procedures.

Dual-use and responsible life sciences

WHO global framework and current program

World Health Organization, Ensuring responsible use of life sciences research. Program page accessed 23 July 2026; it indexes the 13 September 2022 Global Guidance Framework and materials through a 15 June 2026 meeting report.

Frames dual-use risk mitigation as individual and collective, multi-stakeholder work across life sciences and relevant emerging technologies.
Global guidance must be applied with jurisdictional and institutional rules.

Current U.S. policy-status notice

U.S. Department of Health and Human Services, Administration for Strategic Preparedness and Response, Dual Use Research of Concern Oversight Policy Framework. Current-status notice accessed 23 July 2026.

The page states that federal departments and agencies will revise or replace the 2024 DURC/PEPP policy in response to the 5 May 2025 executive order and that the page will be updated when revised policy is available.
Therefore this skill does not present the 2024 policy as a stable current checklist; researchers should consult their institution and current agency materials.

Research method

Parallel CLI searches used focused academic and official-domain queries for:

interactive versus nominal brainstorming and productivity blocking;
NGT, Delphi, and structured expert elicitation;
divergent/convergent idea generation and selection;
NIH rigor, reproducibility, and SABV;
preregistration, Registered Reports, and open-science guidance;
responsible AI, hallucinated citations, homogenization, confidentiality, research integrity, and disclosure;
WHO and U.S. dual-use policy status.

Canonical sources were then fetched with parallel-cli extract using focused objectives for bibliographic metadata, methods, findings, limitations, and current policy status. No research JSON artifacts were saved in the skill.