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In the Shadows: Tales of Cultural Marginalization in Modern Society

2025· article· en· W4415999813 on OpenAlexaff
Innan Sasaki, Eun Young Song, Farah Kodeih, Samira Nazar, Shivaang Sharma, Sophie Alkhaled, Sofiane Baba

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Student and Expatriate Challenges
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsFace (sociological concept)VietnamesePeasantCultural diversityIndigenousConceptual frameworkFoundation (evidence)

Abstract

fetched live from OpenAlex

This panel symposium aims to offer a deeper understanding of the complexities of cultural marginalization and how actors (individuals, organizations, and communities) navigate these challenges. In a world marked by elevated global uncertainty, recent years have seen a noticeable surge in extremism, totalitarianism, and displacement. Within such times, marginalized actors face significant distress due to their lack of power, status, or resources, and cannot effectively defend their interests against more powerful and established actors. As a result, a historical pattern emerges: these marginalized actors experience a loss of both material and symbolic cultural resources, often due to various forms of dominance, whether physical or symbolic. Organizational theorists have increasingly directed their focus toward the concept of marginalization, with a specific emphasis on cultural aspects as a foundation for such marginalization. However, the existing literature in this domain lacks substantial theoretical integration, particularly in understanding the specific dynamics related to “Cultural marginalization” and the strategies actors use to manage these challenges. Cultural marginalization has long-lasting effects, which scholars are still uncovering. Our panelists encompass conceptual and empirical research works allowing us to explore diverse culturally marginalized contexts worldwide, including a marginalized community in Kenya, women in Saudi Arabia, a peasant kin group in rural Korea, and a marginalized Vietnamese community in the UK.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.356
Teacher spread0.323 · 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 designQualitative
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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