Fostering the Human Rights of Migrant Children Through Art and Educational Practices at the United States-Mexico Border
Bibliographic record
Abstract
This paper explores the impact of art and educational practices on fostering the psychosocial well-being and human rights awareness of migrant children residing in shelters at the U.S.-Mexico border. This population often faces acute vulnerabilities due to migratory status and exposure to trauma during transit, including violence, displacement, and family separation. The study emphasizes the collaborative efforts of nongovernmental organizations (NGOs) and early childhood educators within these shelters. Data collection entailed conducting interviews with early childhood educators, art instructors, and literacy mediators who work directly with migrant children. Observations were carried out in reading rooms and multilevel classroom settings, focusing on the structure and flow of activities to ensure they addressed the emotional and psychological needs of the children involved. This research is situated against the backdrop of migrant families and children entrapped at the U.S.-Mexico border due to the Remain in Mexico and Title 42 policies implemented during the COVID-19 pandemic and its aftermath. These policies have resulted in prolonged stays in shelters for asylum-seeking families, creating environments characterized by immigration uncertainty, limited resources, and emotional strain. The findings illuminate the effectiveness of art and educational practices and the crucial role of early childhood educators in helping children process trauma, express their emotions, and develop a sense of identity and agency amidst challenging circumstances.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".