Bibliographic record
Abstract
Syntactic freezing has mainly been approached from a structural point of view, recently though, more cognitive approaches in terms of processing costs have been proposed. One such processing account is the additive account. According to this approach, the freezing effect is best explained as an additive effect of two syntactic processes coming together, rather than being a phenomenon on its own. Another processing account argues that the freezing effect is the result of a prosodic garden path according to which extraction can only take place from a prosodically focused constituent. The current study provides empirical evidence for a less discussed factor contributing to the freezing effect, namely a pragmatic one. The pragmatic account requires frozen sentences to have contextually given referents. If no such referent is present, the sentence becomes less acceptable. The need for such a referent comes from the non- default word order associated with frozen sentences, which often highlights/focuses a certain constituent. Several experiments were run to test the pragmatic account. Based on the results it was concluded that pragmatic factors play a significant role in explaining the apparent freezing effects. Other factors however, seem to contribute to this effect as well since this effect cannot be fully explained in terms of pragmatic factors solely.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".