Bibliographic record
Abstract
A variety of phonological explanations have been proposed to account for why some sounds are harder to learn than others. In this mini-review, we review such theoretical constructs and models as markedness (including the markedness differential hypothesis) and frequency-based approaches (including Bayesian models). We also discuss experimental work designed to tease apart markedness versus frequency. Processing accounts are also given. In terms of phonological domains, we present examples of feature-based accounts of segmental phenomena which predict that the L1 features (not segments) will determine the ease and difficulty of acquisition. Models which look at the type of feature which needs to be acquired, and models which look at the functional load of a given feature are also presented. This leads to a presentation of the redeployment hypothesis which demonstrates how learners can take the building blocks available in the L1 and create new structures in the L2. A broader background is provided by discussing learnability approaches and the constructs of positive and negative evidence. This leads to the asymmetry hypothesis, and presentation of new work exploring the explanatory power of a contrastive hierarchy approach. The mini-review is designed to give readers a refresher course in phonological approaches to ease and difficulty in acquisition which will help to contextualize the papers presented in this collection.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".