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Record W4403071108 · doi:10.1016/j.tics.2024.09.006

Toward an understanding of collective intellectual humility

2024· review· en· W4403071108 on OpenAlex
Elizabeth J. Krumrei-Mancuso, Philip Pärnamets, Steven Bland, Mandi Astola, Aleksandra Cichocka, Jeroen de Ridder, Hugo Mercier, Marco Meyer, Cailin O’Connor, Tenelle Porter, Alessandra Tanesini, Mark Alfano, Jay J. Van Bavel

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

VenueTrends in Cognitive Sciences · 2024
Typereview
Languageen
FieldPsychology
TopicForgiveness and Related Behaviors
Canadian institutionsWestern University
Fundersnot available
KeywordsHumilityPsychologyCognitionCollective intelligenceFocus (optics)Socially distributed cognitionCognitive scienceSocial psychologyCognitive psychologyEpistemologyKnowledge managementNeuroscienceComputer sciencePolitical science

Abstract

fetched live from OpenAlex

The study of intellectual humility (IH), which is gaining increasing interest among cognitive scientists, has been dominated by a focus on individuals. We propose that IH operates at the collective level as the tendency of a collective's members to attend to each other's intellectual limitations and the limitations of their collective cognitive efforts. Given people's propensity to better recognize others' limitations than their own, IH may be more readily achievable in collectives than individuals. We describe the socio-cognitive dynamics that can interfere with collective IH and offer the solution of building intellectually humbling environments that create a culture of IH that can outlast the given membership of a collective. We conclude with promising research directions.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.972
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.005
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.660
GPT teacher head0.554
Teacher spread0.106 · 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