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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 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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.659
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
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 teacher head, 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

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