The lived experiences a selected group of female El Salvadoran child language brokers students in Prince George's County public schools
Bibliographic record
Abstract
Child language brokering is a phenomenon that occurs frequently among children who speak English and their non-English speaking parents and friends. When children interpret for their family members and friends, it is unnoticed by many organizations such as schools, healthcare organizations and businesses. Therefore, child language brokers have missed the opportunity to be recognized for the unusual work that they perform almost daily. Prior research on this topic has primarily focused on Spanish-speaking children from areas such as Mexico and China. This study sought to share the lived experiences of female students from the country of El Salvador. The following research question guided this study: What are the lived experiences of a select group of female El Salvadoran child language brokers in a large urban school district? This question was explored through a survey and in-depth, semi-structured interviews where participants were able to share their experiences when language brokering. Twenty participants participated in the survey and seventeen completed the instrument. Of those individuals, five female students from El Salvador were selected to participate in a semi-structured interview. The survey results were analyzed using descriptive statistics and the interviews were analyzed to gain a thick, rich, description of the participants lived experiences as child language brokers. Four thematic findings emerged from NVivo coding: (a) trust, (b) happiness, (c) helping, and (d) learning. Given the findings of this study, the following three recommendations are offered to child language broker researchers as they seek to understand children who engage in this work: (a) explore why children broker for their friends and how this may impact that relationship. (b) reduce bias in the research by selecting an outside, non-interpreting researcher to collect both the qualitative and quantitative data, and (c) conduct research that answers the following 1) How do children perceive their abilities when language brokering? 2) Do participants think that their interpretation skills are important and valuable? and 3) How do participants perceive their language brokering skills within their school, school district and society?
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 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".