Native speaker constructions in multilingual families
Bibliographic record
Abstract
This thesis addresses the well-established subject area of native speaker research. Previous research into the native speaker has addressed the history of the concept of the native speaker, criticisms of this concept, and alternative terms for native- and non-native speaker. My research investigates how members of multilingual families construct the concept of the native speaker based on their lived experiences. To further guide my research, I investigate how the families establish and enact language beliefs through their constructions of themselves as multilingual subjects. \n \nI have designed small scale, qualitative case studies, which focus on families who share multiple languages as a family. I conducted focus group discussions with three families who share German in their language repertoires, and analyzed the data of two of these families. In the focus group discussions, the family members were encouraged to share their lived experiences as they relate to language use, language ideologies, and sharing multiple languages as a family. \n \nThe results of this study show that the family members construct the concept of the native speaker based on their lived experiences and most often confirm the existing literature on the subject. A common theme throughout the family members’ constructions of the native speaker is the influence of the monolingual bias. Through the focus on native speaker constructions in the context of a multilingual family, the influence of the family members’ native speaker constructions on their family language polices (FLP) is noted and briefly explored.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".