Bibliographic record
Abstract
Abstract With respect to post-childhood acquisition of an additional language (L2), it has sometimes been suggested that word class is an important co-determinant of ‘word learnability’, defined as the speed and thoroughness with which a once unknown L2 word can be integrated into a learner’s lexicon. This chapter reviews evidence for this suggestion, largely with respect to the open word classes noun, verb, and adjective, since these are the classes that have received the most attention in empirical research. It begins by discussing variables other than word class which empirical researchers have identified as notable co-determinants of word learnability; for example, semantic variables such as concreteness (i.e. the perceptibility of the referent) and degree of polysemy, form variables such as length, the usage-based variable item frequency, and interlingual or partly interlingual variables such as cognateness and pronounceability. Then, with respect to the difficulty of isolating an effect of word class on word learning, the chapter gives examples of how experimental studies which seemed to have revealed a word-class effect are in fact at least as likely to have indicated an effect of some other variable. There seems to be no compelling evidence for an independent word-class effect on word learnability.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".