Race/ethnicity, discrimination, and confidence in order institutions
Bibliographic record
Abstract
Purpose The purpose of this paper is to propose and test a conceptual model that explains racially/ethnically differential confidence in order institutions through a mediating mechanism of perception of discrimination. Design/methodology/approach This study relies on a nationally representative sample of 1,001 respondents and path analysis to test the relationships between race/ethnicity, multiple mediating factors, and confidence in order institutions. Findings Both African and Latino Americans reported significantly lower levels of confidence compared to White Americans. People who have stronger senses of being discriminated against, regardless of their races, have reduced confidence. A range of other cognitive/evaluative variables have promoted or inhibited people’s confidence in order institutions. Research limitations/implications This study relies on cross-sectional data which preclude definite inferences regarding causal relationships among the variables. Some measures are limited due to constraint of data. Practical implications To lessen discrimination, both actual and perceived, officials from order institutions should act fairly and impartially, recognize citizen rights, and treat people with respect and dignity. In addition, comprehensive measures involving interventions throughout the entire criminal justice system to reduce racial inequalities should be in place. Social implications Equal protection and application of the law by order institutions are imperative, so are social policies that aim to close the structural gaps among all races and ethnicities. Originality/value This paper takes an innovative effort of incorporating the currently dominant group position perspective and the injustice perspective into an integrated account of the process by which race and ethnicity affect the perception of discrimination, which, in turn, links to confidence in order institutions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".