Superiority in English and German: Cross‐Language Grammatical Differences?
Bibliographic record
Abstract
Abstract Do the grammars of English and German contain a ban on moving the lower of two wh‐phrases (Superiority), or is the lower acceptability due simply to the complexity of processing the longer dependency that results when the lower wh‐phrase is moved? The results of four acceptability‐judgment studies suggest that a pure processing account is inadequate. Crossing wh‐dependencies lower the acceptability of both German and English questions but with a significantly larger penalty in English than in German (experiment 1). The larger penalty in English cannot be attributed to greater sensitivity to violations in English, because relative clause island violations result in similar effects in the two languages (experiment 2). A pure processing account might claim long dependencies are easier to process in German than in English because of richer case, but a control experiment did not support this possibility (experiment 4). We suggest that moving the lower of two wh‐phrases is banned in the grammar of English but not in the grammar of German. This predicts that there should be a penalty for crossing dependencies in English even in helpful (Bolinger) contexts, as confirmed in experiment 3, and even in short easy‐to‐process sentences, as confirmed by simple six‐word sentences in Clifton, Fanselow & Frazier 2006. Finally, if German grammar does not contain a ban on crossing, it is not surprising that the penalty in German is smaller than in English or that like animacy of the two wh‐phrases plays a larger role in German than in English because feature similarity generally gives rise to difficulty in processing, whereas in English a grammatical ban on crossing will reduce acceptability regardless of whether there is processing difficulty.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".