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Record W4236835618 · doi:10.32920/ryerson.14661735

The impact of anti-black racism on black males

2021· preprint· en· W4236835618 on OpenAlexaffabout
Rick Acheampong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicQualitative Research Methods and Ethics
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsRacismOppressionRacializationGender studiesSociologyWhite (mutation)Critical race theoryCriminalizationPsychometrics of racismCriminologyRace (biology)Political sciencePoliticsLaw

Abstract

fetched live from OpenAlex

This paper examines the lived experiences of racialization, oppression, criminalization, and discrimination upon Black males living in Canada. The theoretical framework for my research was comprised of anti-Black racism and Critical Race Theory. These frameworks guided my research into the lived experiences of anti-Black racism against Black males. The research study used a narrative approach where Black male participants shared stories of anti-Black racism. The research entailed asking participants open-ended questions about their lived experiences of anti-Black racism and the impact it has had on their lives. The findings from the research highlighted how marginalized and racialized groups in Canada practice anti-Black racism towards Black people within public spaces. It also showed the anti-Black racism that police officers engage in against Black males within institutions that have a history of being anti-Black. The findings showed the lack of self-awareness and white supremacist beliefs that racialized groups embody when perpetuating anti-Black racism. Also, it highlighted the continuous practice by police officers who racially profile and incarcerate Black males due to their skin color. The conclusion from the study highlighted that Black males continue to experience anti-Black racism within agencies that make up the criminal justice system and the anti-Black racism that marginalized groups engage in when interacting with Black bodies.

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)0.014
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.265
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0140.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.322
GPT teacher head0.598
Teacher spread0.276 · 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.

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
Published2021
Admission routes2
Has abstractyes

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