Bibliographic record
Abstract
Abstract Schwartz and Sprouse (2021) argue against property-by-property Transfer ( Westergaard, 2021a , b ) and for wholesale transfer ( Rothman, 2015 ) into a third language grammar by questioning the cognitive plausibility of “extracting a proper subpart from the … grammar and using that proper sub-system as the basis for a new cognitive state.” I will argue that the insights from the approaches of López (2020) ; Lightfoot (2020) ; Dresher (2018) , and Westergaard (2021a) when applied to empirical data from L3 English data from L1 Arabic/L2 French speakers, give us reason to question Schwartz and Sprouse’s defence of wholesale transfer, and its typological underpinnings. We can set the study of L3A in a larger context which can unify domains such as the acquisition of phonology and syntax via a unified approach to parsing. By invoking an underspecified, minimal UG, primary linguistic data, and domain-general third factors which act in concert to parse the E-language to select structures, we can capture the underlying similarity of first, second, and third language acquisition. Parsing proceeds in an error-driven fashion, structure by structure, drawing on the Integrated I-language and UG options found in a Repository. In essence, this approach renders the wholesale/property-by-property distinction a false dichotomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".