Perceived discrimination in leisure settings in Latino urban communities
Bibliographic record
Abstract
This study explored: (1) whether Latino residents of two highly segregated neighbourhoods in Chicago, IL, USA, experienced or witnessed any discriminatory incidents in leisure settings; (2) what were the most frequent places and types of discrimination they encountered; (3) who were the perpetrators of discriminatory acts and (4) how people responded to discrimination. Moreover, Latinos own interracial/interethnic attitudes toward members of other ethnic/racial groups were examined. Data were collected with the use of surveys and focus groups. The results suggest perceived discrimination is an important constraint on recreation behaviour among Latino urban residents. The findings revealed that Latinos most often experienced discrimination from African Americans and Whites visiting the parks, as well as from law enforcement officers. Verbal harassment from other recreationists, being stopped and searched by police and being denied a service or being given substandard service were named most often as the types of discrimination. Survey respondents indicated that they responded to discrimination by visiting the locations with a group of people or by notifying the police, whereas focus groups participants suggested withdrawal was the most often employed tactic. The findings also suggested a disconnect between Latinos' interracial/interethnic attitudes at the individual and group levels. Although the interviewees reported having positive to neutral interracial/interethnic attitudes, they were willing to acknowledge the existence of prejudicial attitudes among Latinos at the group level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".