Learning functional categories in a second language on initial exposure: Classifiers
Bibliographic record
Abstract
We explore the interaction of linguistic and visual stimuli in the learning of nouns and classifiers in a novel language on first exposure. To interpret pictures, knowledgeable language users often rely on language that suggests what in the picture a speaker might be talking about. On first exposure to another language, this is not possible. It is often assumed that visual stimuli support inferences needed to learn the meanings of words. Within the Conceptual Semantics framework (Jackendoff, 1983, 2010, 2015), both noun phrases and nominal classifiers may express ontological categories such as THING, INDIVIDUAL, AMOUNT (of THINGs), (THING-)SHAPE, (THING-)SIZE, and (THING-)PROPERTY. Crucially, ontological categories may be independently accessed via visual stimuli to guide initial associations of conceptual representations and sound forms. We provide preliminary data showing that it is possible for adults to make such initial associations. Even with complex pictures, noun learning is comparatively easy. Classifier learning is much harder because it requires learners to extract “contrasts” across multiple stimuli.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".