Quantitative analyses in a multivariate study of language attrition: the impact of extralinguistic factors
Bibliographic record
Abstract
Most linguistic processes — acquisition, change, deterioration — take place in and are determined by a complex and multifactorial web of language internal and language external influences. This implies that the impact of each individual factor can only be determined on the basis of a careful consideration of its interplay with all other factors. The present study investigates to what degree a number of sociolinguistic and extralinguistic factors, which have been previously demonstrated or claimed to be relevant in the context of language attrition, can account for individual differences in first language (L1) proficiency. Data were collected from attriting populations with German as their L1: one in a Dutch language context ( n = 53) and one in a Canadian English setting ( n = 53). These groups were compared to a reference group of Germans in Germany ( n = 53). Overall, the proposed outcome measures (derived from both formal tasks and a free speech task) are argued to be stable and valid indicators of attrition effects. The predictor variables under investigation are shown to fall into several reliable factor groups, for example, identification and affiliation with L1, exposure to German language and attitude towards L1. These are the factor groups that have, so far, been considered the most important for the process of L1 attrition or maintenance. However, the predictive power exercised by these factor groups in the present study is shown to be relatively weak.
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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 | 0.036 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".