Using a sociological conceptualization of stigma to explore the social processes of stigma and discrimination of children in street situations in western Kenya
Bibliographic record
Abstract
BACKGROUND: The leading causes of street involvement worldwide are poverty, family conflict, and abuse. A common misconception is that street involvement is due to delinquency, a belief leading to social exclusion and social inequality for children in street situations (CSS). Exploring community perceptions of CSS and the reproduction of social difference and inequalities can help reduce stigma and discrimination. OBJECTIVE: To explore how stigma and discrimination of CSS was produced and reproduced in specific contexts of culture and power. PARTICIPANTS AND SETTING: Social actors including CSS, healthcare providers, children's officers, and police officers in western Kenya. METHODS: Using a sociological conceptualization of stigma, this qualitative study explored the stigmatization processes that take shape in specific contexts of culture and power. We conducted 41 in-depth interviews and 7 focus group discussions with a total of 100 participants. RESULTS: CSS were often labeled "chokoraa" or garbage picker, a label linked to undesirable characteristics constituting "evils" in society and stereotyped beliefs that they were "delinquents," reinforcing their "otherness" and devalued social status. CSS experienced individual and structural discrimination leading to exclusion from social and economic life. CONCLUSION: CSS were stigmatized when labeled, set apart, and linked to negative characteristics leading to their experience of status loss and discrimination. CSS's differentness and devalued status served to limit their access to societal resources and deemed them unworthy of equal rights. Interventions involving various social actors are needed to challenge negative stereotypes, reduce stigma, and uphold CSS's human rights.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.024 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".