Learning Island-insensitivity from the input: A corpus analysis of child- and youth-directed text in Norwegian
Bibliographic record
Abstract
Norwegian allows filler-gap dependencies into relative clauses (RCs) and embedded questions (EQs) – domains that are usually considered islands in other languages. We conducted a corpus study on youth-directed reading material to assess what direct evidence Norwegian children receive for filler-gap dependencies into islands. Results suggest that the input contains examples of filler-gap dependencies into both RCs and EQs, but the examples are significantly less frequent than long-distance filler-gap dependencies into non-island clauses. Moreover, evidence for island violations is characterized by the absence of forms that are, in principle, acceptable in the target grammar. Thus, although they encounter dependencies into islands, children must generalize beyond the fine-grained distributional characteristics of the input to acquire the full pattern of island-insensitivity in their target language. We consider how different learning models would fare on acquiring the target generalizations and speculate on how the observed distribution of acceptable filler-gap dependencies reflects the interaction of syntactic, semantic, and pragmatic conditions.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".