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Record W4362705354 · doi:10.1111/gwao.12993

What is the real perversity of racism?

2023· article· en· W4362705354 on OpenAlexaff
Ajnesh Prasad

Bibliographic record

VenueGender Work and Organization · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Organizational Studies
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsRacismSubject (documents)SociologyImpossibilityRacializationIdentity (music)AestheticsGender studiesEpistemologyRace (biology)PhilosophyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Racism is inscribed onto the mind of the racialized subject. This essay represents my attempt at making sense of the psychological costs levied by the racism that materializes from living in a culture of hegemonic whiteness. My analysis is informed by Frantz Fanon's writings and, particularly, his concept of corporeal malediction. Corporeal malediction captures the unique reality of the racialized subject—one that is different from the systemic discrimination and violence encountered by other socially disenfranchised groups. Theoretically framed by Fanon's “postcolonial” thought, and anecdotally informed by my own experiences as well as my past research on race and racism in organizations, I pose the question: What is the real perversity of racism? I argue that the real perversity of racism is to be found in the psychological damage that it poses on the ontology of the racialized subject. This psychological damage on the racialized subject unfolds through myriad trajectories, three of which I identify in this essay: (1) the (dis)coloring of social reality, (2) the impossibility of assimilation, and (3) the hybrid identity problematic. While management and organization studies researchers have expended much energy in unraveling the institutional and the organizational paths through which racism is enacted, there has been a dearth of scholarly research on its devastating impact on the psychology of the individual. This essay aims to flesh out this corporeal phenomenon.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.424

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.021
GPT teacher head0.207
Teacher spread0.186 · 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 designObservational
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

Citations13
Published2023
Admission routes1
Has abstractyes

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