Textual Effects in Compound Processing: A Window on Words in the World
Bibliographic record
Abstract
We sought to move beyond single word and sentence processing experiments in order to examine textual effects on the processing of compound words in English. We developed minimal texts (sentences pairs that together constitute a story) that had neutral, semantic or lexical relations between the last word of the first sentence and the second word of the second sentence (which was always a compound noun). This generated minimal text triplets that differed only in the last word of the first sentence (e.g., “She walked down to the path/river/water. The waterfall roared in the distance”). Four experiments were conducted with a total 143 native speakers of English. Experiment 1 employed a Modified Maze Task to identify cross-sentence effects on compound processing. Sentence pairs with lexical links differed from those with semantic links, which, in turn differed from neutral pairs, providing evidence of cross-sentence influence on compound processing. In Experiments 2a, 2b, and 2c, we examined compound production using typing tasks. Results indicated that morphological effects found in single word typing persisted in text typing. In addition, constituent priming effects on typing were seen in both single word typing and sentence typing. Finally, morphological effects were correlated with overall story ratings. We thus conclude that morphological effects are not restricted to single word processing, but rather reflect the dynamics of real-world language processing.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".