Word processing through lexical decision in Brazilian Portuguese
Bibliographic record
Abstract
Abstract This study investigates word processing in Brazilian Portuguese, focusing on blends, which juxtapose or overlap (W)ords and/or (C)lips (e.g. portunhol = (portu)guês ‘Portuguese’ + espa(nhol) ‘Spanish’). Blends present intriguing theoretical and empirical challenges to models of morphological analysis, morphological processing, lexical access, and the mental lexicon. Most research on blends has been conducted in languages other than Portuguese. This study addresses this gap by exploring the processing of blends in Brazilian Portuguese through a behavioral lexical decision experiment. We manipulated blends in constituent structure and grammatical structure, considering (H)ead and (M)odification. Additionally, we compared blends against words with various morphological structures, such as derived complex words containing prefixes (e.g., [des]acordo ‘disagreement’) or suffixes (e.g., cozinh[eiro] ‘cook’), and monomorphemic simplex words. We also included simplex and complex pseudowords (e.g., [acont]arago ; dador[eiro] ) and nonwords (e.g., sfaricrelj ) in the experiment. Accuracy and reaction time results suggest that blends are accepted and processed differently from simplex and complex words, resembling pseudowords. This study contributes to a deeper understanding of blend description and processing, providing valuable insights into lexical access, enhancing theoretical and empirical comprehension of morphological 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".