A nonce investigation of a possible conjunctive default for disjunction
Bibliographic record
Abstract
Our study explores whether there is a conjunctive default in the interpretation of disjunction, focusing on Romanian children’s and adults’ understanding of nonce functional words. We investigate how participants interpret novel connectors such as mo and mo...mo, which could theoretically correspond to ‘(both) A and B’, ‘(either) A or B’, or ‘A not B’ / ‘neither A nor B’. Our results reveal that both adults and children overwhelmingly assign a conjunctive meaning to these nonce words. This suggests the existence of a conjunctive default in interpreting unknown operators linking two elements, which could explain why children have sometimes been found to interpret disjunctions as conjunctions in previous studies (Singh et al. 2016, Tieu et al. 2017, Bleotu et al. 2023). In particular, we discuss how this conjunctive default may influence Romanian children’s interpretation of complex disjunctions such as fie...fie, potentially explaining why they treat these constructions conjunctively. Importantly,our findings also raise broader questions about why certain logical interpretations are favored over others, and whether frequency or cognitive simplicity can drive such biases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 0.001 |
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".