Comparing Island Effects for Different Dependency Types in Norwegian
Bibliographic record
Abstract
Recent research suggests that island effects may vary as a function of dependency type, potentially challenging accounts that treat island effects as reflecting uniform constraints on all filler-gap dependency formation. Some authors argue that cross-dependency variation is more readily accounted for by discourse-functional constraints that take into account the discourse status of both the filler and the constituent containing the gap. We ran a judgment study that tested the acceptability of wh-extraction and relativization from nominal subjects, embedded questions (EQs), conditional adjuncts, and existential relative clauses (RCs) in Norwegian. The study had two goals: (i) to systematically investigate cross-dependency variation from various constituent types and (ii) to evaluate the results against the predictions of the Focus Background Conflict constraint (FBCC). Overall we find some evidence for cross-dependency differences across extraction environments. Most notably wh-extraction from EQs and conditional adjuncts yields small but statistically significant island effects, but relativization does not. The differential island effects are potentially consistent with the predictions of the FBCC, but we discuss challenges the FBCC faces in explaining finer-grained judgment patterns.
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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 | 0.006 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".