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Emotion, Risk, and Identity: Exploring Self-Work in Justice Organizing and Systemic Navigation

2025· article· en· W4416005665 on OpenAlexaffabout
Charlotte M. Karam, Rich DeJordy, Yasmeen Makarem, Patil Yessayan, Mariam Omar, Olfat Khattar, Fida Afiouni

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicEmotional Labor in Professions
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPerspective (graphical)Context (archaeology)CreedIdentity (music)NarrativeMeaning (existential)Power (physics)Social dynamicsEconomic JusticeEmpowerment

Abstract

fetched live from OpenAlex

Social problems, viewed as social-symbolic objects, are inherently dynamic and susceptible to change through the deliberate intentional actions of engaged actors. This perspective highlights the recursive nature of the relationships between the work and the objects it targets, underscoring a continuous interplay of influence and change (Caza et al., 2021; Weick et al., 2020). Social-Symbolic Work (SSW) can offer one perspective that enables comprehensive observation of various forms of work and their interplay, which are instrumental in shaping the construction of social problems (Karakulak and Lawrence, 2023). For example, SSW examines how individuals create, modify, and maintain meaning through symbols, practices, and narratives in social contexts. In the context of social justice, this framework helps to explore how individuals navigate personal transformation and identity construction while interacting with systemic forces (Kouamé et al., 2022). These efforts not only involve internal emotional labor (Barberá-Tomás et al., 2019) but also actively reshape symbolic meanings, identities, and power structures. We are particularly interested in how individuals engage in reflective practices and take deliberate actions to shape, understand, or maintain their identities, emotions, and roles within larger social and organizational structures. In this symposium, we thus focus on exploring the discursive dimension of Self-Work (Lawrence & Phillips, 2019) within the context of navigating and advancing social justice agendas (e.g., DeJordy et al., 2020; Gutierrez, et al., 2010), with a particular focus on the identity, career, and emotional work individuals undertake as they confront and/or seek to transform oppressive systems (e.g., Creed et al., 2010; Creed et al, 2014; Lok et al., 2019). Lawrence and Phillips (2019) describe Self-Work as an act of shaping one’s social-symbolic dimension, which significantly influences both the individual and those around them. While self-work encompasses three key dimensions - discursive, relational, and material - our primary focus is on the discursive dimension, while also acknowledging the influence of the other two. Specifically, we explore how individuals shape their identities by constructing narratives informed by their relationships and utilizing various material resources to make these narratives tangible. As Lawrence and Phillips (2019) suggest, this approach moves beyond strictly cognitive views of the self, allowing for a more nuanced understanding of selves with unique capabilities and constraints. By focusing on the narrative aspect of self-work, we refer to the significant effort individuals invest in telling stories either about themselves or in which they play a prominent role, revealing key insights about their identities. Enfranchising Grief: An Ethic of Care in Collective Action Author: Richard DeJordy; Rochester Institute of Technology Narratives of Risk in Women's Economic Empowerment Movements in MENA Author: Charlotte M. Karam; University of Ottawa Guilt in Women’s Career Narratives: Women's Self Work in Conflict Zones Author: Patil Yessayan; University of Ottawa Divided from Within: Intra-Movement Framing Tensions as Sites of Contestation Author: Yasmeen Makarem; American University of Beirut

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)0.002
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 score0.733
Threshold uncertainty score0.392

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.033
GPT teacher head0.331
Teacher spread0.298 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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