Bibliographic record
Abstract
The Attrition via Acquisition (AvA) model unifies acquisition and attrition by proposing that intake to the inference engine can come from the first language (L1) or the second language (L2). What this model does not specify, however, is the specific psycholinguistic mechanisms that can lead to attrition nor how partial representation may come about. This study expands the AvA model by incorporating activation as a key mechanism and precursor to attrition, and tests the proposal with cross-linguistic priming in bilinguals. We present data from two studies of Finnish and Estonian/English in a community of long-term L1 Finnish emigrants in USA, Canada, Australia, and Estonia. The target condition were the alternation between the marked and unmarked form of the accusative, and marked accusative and partitive, since these two morphemes have been previously documented to suffer attrition in contact with English. Although results did not indicate cross-linguistic priming from either English or Estonian into Finnish, there was evidence of within-language priming in the English–Finnish bilinguals. These findings support the incorporation of activation into the model, but also suggest that the source of attrition for morphology in particular might not come from the L2.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".