Native Language Attrition Among Immigrants
Bibliographic record
Abstract
This study was a literature review, and it focused on native language attrition among immigrants. For many years, immigrant families have moved to places such as the United States and Europe as well as Canada because of war, abuse, persecution, environmental degradation, and poverty in their home countries. Once immigrant families arrived in their designated country, their children were placed in school. Due to the struggle with the language barrier, kids and adults quickly started to focus extensively on learning the English language. The research occurred due to parents' raised concerns regarding their children's inability to communicate in their first language (L1) with families after learning English. Having worked with adult language learners at Project English and Giving Plus Learning, I witnessed the rise in parents' frustration, concern, and confusion when their children could no longer speak, formulate sentences, or enjoy conversation with relatives in their home language. These parents did not understand why their children constantly spoke in English and rejected their native tongue. The literature review aimed to shed light on the significance of maintaining L1 among immigrants' families while learning and speaking English. The review brought together all the available resources related to my project topic in place, and the resources were evaluated closely. In the review, there were six essential themes divided into sections. Each chapter focused on the method and findings of the themes. The research studies highlighted that L1 attrition could occur instantly when the L1 is not being utilized. The studies showed that age plays a vital role in L1 loss because the earlier children were exposed to the English language, the higher the possibility of L1 deterioration, particularly with children who began English from age three to seven. Older children who spoke the native language from birth to age 12 and then learned English continued communicating in the L1 much better. The studies further pointed out that the English immersion environment helped immigrants practice and improve their communication in the L2. However, the L1 was hindered because immigrants' children had less opportunity to speak in the and more in L2 as if it was their L1.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".