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Emotions, Institutions, and Power

2025· reference-entry· en· W4407663885 on OpenAlexaff
Maxim Voronov, Lee C. Jarvis

Bibliographic record

VenueOxford Research Encyclopedia of Business and Management · 2025
Typereference-entry
Languageen
FieldSocial Sciences
TopicPolitical Philosophy and Ethics
Canadian institutionsYork University
Fundersnot available
KeywordsPower (physics)PhysicsThermodynamics

Abstract

fetched live from OpenAlex

Abstract In the intricate relationship between emotions, institutions, and power, emotions are central to both reinforcing and challenging institutions and animating institutional processes. The literature at the intersection of emotions and institutions highlights how emotional experiences influence engagement with higher-order institutions, shaping practices and the symbolic systems that imbue these activities with meaning. Emotions are not just personal experiences but are deeply embedded in institutional dynamics, motivating continued adherence to institutionalized practices and values, as well as being strategically deployed to theorize, diffuse, or undermine the status quo. Institutions, as mechanisms of social control, pre-interpret the world for people, shaping their lived experiences and telling them how to think and feel. Emotions are a key pathway through which institutions exert this control, making power a more central concern in institutional theory. The distinction between systemic and episodic power is leveraged to help illuminate this dynamic. Systemic power operates through less visible means, such as institutional norms and ideological resources, while episodic power involves overt acts of coercion and manipulation. A review of existing literature highlights how emotions are implicated in the exercise of both systemic and episodic power. For instance, institutions exert systemic power over their inhabitants, conditioning emotional practice by valuing and prescribing certain emotions, creating emotional registers that dictate the legitimate use and expression of emotions within a particular institutional context. However, emotions can also drive resistance against institutional norms. Anger, for example, can signal dissatisfaction with the status quo and motivate efforts to reform or challenge institutional structures. Shame, typically associated with conformity, can also prompt reflection and resistance against institutional norms. On the other hand, extant literature suggests episodic power is clearly implicated in the ways in which institutional arrangements are created, stabilized, and changed. Episodic power is exercised through eliciting emotions in others, often through emotionally resonant rhetoric or other means such as visuals, spaces, and rituals. These emotionally charged appeals can mobilize support for institutional projects or instigate resistance. Furthermore, shared emotional experiences can foster collective identities that drive institutional change. Additionally, the literature speaks to the way in which emotional regulation—or the reflective control of emotional experiences and displays to have consciously intended effects on others—is integral to the exercise of episodic power, with several studies suggesting that regulation is a crucial enabling mechanism for effectively implementing a given institutional project. Future research should explore the interaction between systemic and episodic power, the material and temporal dimensions of emotions in institutions, and the role of unconscious processes in institutional dynamics.

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)0.006
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: Other · Consensus signal: Other
Teacher disagreement score0.009
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.017
Scholarly communication0.0090.006
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.099
GPT teacher head0.393
Teacher spread0.295 · 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
GenreOther

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".

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Citations2
Published2025
Admission routes1
Has abstractyes

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