On the acquisition of <em>either</em> and <em>too</em>
Bibliographic record
Abstract
This paper presents an experimental investigation of how English-learning children acquire the additive discourse particles either and too. In the target grammar these items exhibit near-complementary distribution conditioned on the polarity of their host sentence. The path leading to that grammar appears to be rather intricate. We present comprehension data showing that for an extended period of time (3–5 ya) learners find both items acceptable in both polarity environments, exhibiting only a weak adult-like tendency of preferring either in negative and too in positive sentences. At 6 ya, their grammar appears categorical wrt. either in that they no longer tolerate it in positive sentences while still exhibiting only a weak dispreference for too in negative environments. These findings are even more striking in the context of production data. We find that child-directed speech is essentially categorical, providing unambiguous evidence for the adult grammar. Moreover, we find essentially categorical, adult-like use of either and too in child production from the earliest stage of development. These observations raise a number of challenges for theories of either and too and for approaches to learning focus particles more generally. Perhaps most strikingly, the protracted insensitivity of the learner's grammar to accumulation of unambiguous evidence constitutes a novel argument from the abundance of evidence for encapsulated learning.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".