MétaCan
Menu
Back to cohort

Interpersonal Interactions at Work: A Biological Perspective

2025· article· en· W4416003221 on OpenAlexaff
Mark van Vugt, Adam M. Kleinbaum, Stefan Volk, Danni Wang, Frédéric Ooms, Yair Berson, Alon Burns

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldNeuroscience
TopicUndergraduate Neuroscience Education and Research
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPerspective (graphical)Interpersonal communicationInterpersonal relationshipOrganizational behaviorBridge (graph theory)Session (web analytics)

Abstract

fetched live from OpenAlex

This symposium will explore the biological foundations of interpersonal interactions in workplace settings, focusing on how neurobiological and physiological processes influence trust, communication, and collaboration. Panelists will share insights from cutting-edge research on topics such as neural synchrony, evolutionary perspectives on leadership, circadian rhythms, and physiological alignment, offering novel perspectives on team dynamics, decision-making, and productivity. The session will aim to bridge biological mechanisms with organizational behavior frameworks, fostering interdisciplinary discussions that illuminate the role of biology in shaping individual and collective outcomes in organizations. Through this dialogue, the symposium will highlight theoretical advancements, practical applications, and future research directions for integrating biology into organizational science.

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 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)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0030.010
Scholarly communication0.0080.006
Open science0.0010.002
Research integrity0.0030.003
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 head0.106
GPT teacher head0.391
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueAcademy of Management ProceedingsSame topicUndergraduate Neuroscience Education and ResearchFrench-language works237,207