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Record W4379656171 · doi:10.54254/2753-7048/5/20220692

Exploring Discrimination against Blacks in the Workplace

2023· article· en· W4379656171 on OpenAlexaff
Wenqi Tao

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2023
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPhenomenonRace (biology)Reading (process)White (mutation)RacismGovernment (linguistics)Work (physics)Field (mathematics)Public relationsPsychologySocial psychologySociologyGender studiesPolitical scienceLawEngineering

Abstract

fetched live from OpenAlex

In today's society, although the phenomenon of racial discrimination is much better than before, it is still everywhere in the world. In the current workplace, many black workers are treated differently than others because of their races. They have separate work areas, separate bathrooms, and even the utensils they use are frowned upon by others. Many times blacks suggestions are not accepted. The article will study whether blacks are subjected to racial discrimination in the workplace from six different aspects. Research in this field can help people understand the special treatment that black people receive in the workplace. In addition, the government should pay attention to this phenomenon and make some new legal measures to control and improve the unfair treatment of black people in all aspects of life. Therefore, it is necessary to find and compare the treatment of different races in the workplace. By reading previous relevant articles, this article summarizes and refines the treatment of black people in different aspects of the workplace. It finds that people of this kind of race, no matter how skilled they are or how well they do in their field of work, are treated very poorly compared with white people.

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)0.002
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 score0.269
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.182
GPT teacher head0.452
Teacher spread0.271 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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