On the availability of inverse scope reading in Dutch doubly-quantified thetic sentences
Bibliographic record
Abstract
This study investigates the availability of inverse scope readings in Dutch doubly quantified sentences, focusing on thetic (general rule-based) versus categorical (individual-focused) contexts. Although previous research suggests that inverse scope readings are rarely available in Dutch simple transitive sentences, we show that such readings are available and that the availability of such readings correlates with the theticity and tense of the doubly quantified sentences. Building on Mandarin experiments by Wu (2019, 2023), the study adopts a 2x2 design for Dutch, manipulating utterances’ tense (present vs. past) and context (thetic vs. categorical). The results of a scope judgment task (N=113) show that inverse scope is significantly more acceptable in thetic contexts than in categorical ones, and marginally less acceptable in sentences about the past compared to the present. These results challenge the isomorphic scope interpretation typically proposed for Dutch doubly quantified simple transitions and contradict the claim that indefinite subjects cannot be interpreted non-specifically in Dutch. Furthermore, this study provides cross-linguistic evidence for the impact of the thetic-categorical distinction on the quantifier scope and calls for further investigation into the interaction between temporal marking, thetic-categorical distinction, and scope interpretations.
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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 | 0.003 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".