Bibliographic record
Abstract
In this article, a number of broad questions related to the acquisition of consonant clusters in a second language are investigated.Drawing on the structural relations and phonological principles of Government Phonology, it is argued that the behaviour of second language learners can be accounted for by a top-down, left-to-right phonological parser.Appealing to a model of cognitive architecture, it is demonstrated that one can account for the different behaviours of speakers of languages that share the trait of lacking tautosyllabic clusters (Korean and Finnish) when learning a language which allows such clusters (English).Properties of the LI segmental inventory and a licensing strength scale are proposed to explain why Finnish learners have less trouble than Korean learners when acquiring English clusters. ResumeCet article aborde certaines questions generates relatives a 1'acquisition de groupes consonantiques en langue seconde.Sur la base des relations structurelles et des principes phonologiques de la theorie du gouvernement, il y est soutenu que le comportement des apprenants en langue seconde peut s'expliquer par l'entremise d'un analyseur phonologique operant de haut en bas et de gauche a droite.En faisant appel a un modele d'architecture cognitive, il est possible de rendre compte des comportements divergents d'apprenants ayant en commun 1'absence de groupes tautosyllabiques dans leur langue maternelle (le coreen et le finnois) lors de l'apprentissage d'une langue qui possede de tels groupes (Vanglais).Les proprietes de l'inventaire segmental de la LI de meme qu'une echelle de pouvoir de legitimation sont invoquees pour expliquer pourquoi les locuteurs du finnois ont moins de difficulte que les locuteurs du coreen a acquerir les groupes consonantiques de 1'anglais.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.701 | 0.656 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".