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Record W4220983907 · doi:10.21203/rs.3.rs-1071626/v1

Building a Transdisciplinary Expert Consensus on the Cognitive Drivers of Performance Under Pressure: An International Multi-panel Delphi Study

2022· preprint· en· W4220983907 on OpenAlexafffund
Lucy Albertella, Rebecca Kirkham, Amy B. Adler, John Crampton, Sean P. A. Drummond, Gerard J. Fogarty, James J. Gross, Leonard D. Zaichkowsky, Judith P. Andersen, Paul T. Bartone, Danny Boga, Jeffrey Bond, Tad T. Brunyé, Mark J. Campbell, Liliana G Ciobanu, Scott R. Clark, Monique F. Crane, Arne Dietrich, Tracy Jill Doty, James E. Driskell, Ivar Fahsing, Stephen M. Fiore, Rhona Flin, Joachim Funke, Justine M. Gatt, Peter Hancock, Craig A. Harper, Andrew Heathcote, Kristin J. Heaton, Werner Helsen, Erika K. Hussey, Robert B. Jackson, Sangeet Khemlani, William D. S. Killgore, Sabina Kleitman, Andrew M. Lane, Shayne Loft, Clare MacMahon, Samuele Marcora, Frank McKenna, Carla Meijen, Vanessa Moulton, Gene Moyle, Eugene Nalivaiko, Donna O’Connor, Dorothea O’Conor, Debra Patton, Mark Piccolo, Coleman Ruiz, Linda Schücker, Ronald E. Smith, Sarah Smith, Chava Sobrino, Melba C. Stetz, D B Stewart, Paul Taylor, Andrew Tucker, Haike E. van Stralen, Joan N. Vickers, Troy A. W. Visser, Frederick Walker, Mark W. Wiggins, Mark C. Williams, Leonard Wong, Eugene Aidman, Murat Yücel

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldSocial Sciences
TopicDelphi Technique in Research
Canadian institutionsUniversity of CalgaryUniversity of Toronto
FundersCilagDefence Science and Technology GroupNational Health and Medical Research CouncilCanadian Institutes of Health ResearchFederal Aviation AdministrationServierH. Lundbeck A/SDepartment of Industry, Innovation and Science, Australian GovernmentEisaiDepartment of Science and Technology, Ministry of Science and Technology, IndiaMonash UniversityScience Foundation IrelandMedical Research CouncilWilson FoundationAustralian Research CouncilDepartment of Defence, Australian GovernmentUniversity of SydneyAustralian GovernmentEuropean Regional Development FundU.S. Department of Defense
KeywordsDelphi methodDelphiCognitionKnowledge managementComputer sciencePsychologyArtificial intelligence

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

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)0.137
metaresearch head score (Gemma)0.169
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.137
Threshold uncertainty score0.722

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1370.169
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0080.004
Scholarly communication0.0060.008
Open science0.0030.011
Research integrity0.0040.004
Insufficient payload (model declined to judge)0.0030.001

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 head0.464
GPT teacher head0.575
Teacher spread0.111 · 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations5
Published2022
Admission routes2
Has abstractno

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