Truth Functions and Memory in English Language Learners
Bibliographic record
Abstract
English Language Learners (ELLs) are consistently found to overuse, misunderstand, and misuse connectives in the English language (Bolton et al., 2002; Chen, 2006; Hinkel, 2002; Ozono & Ito, 2003; Zhang, 2000) and current research has not investigated whether this misunderstanding effects the memory of claims. The primary goal of the present study was to examine whether knowledge of truth-functional connectives is related to conjunctive bias in ELL students. Using a within-subjects design, the effects of instruction in truth-functional connectives on conjunctive bias in nine ELL students were investigated. Repeated measures analyses of variance (ANOVA) revealed an elimination of conjunctive bias following explicit instruction in truth functions. Further tests were also conducted to validate instruments for measuring conjunctive bias, the understanding of truth functions, and to evaluate conjunctive bias and the understanding of truth functions among 29 ELL’s. The findings have significant pedagogical implications related to the justification of including instruction in truth functions in language curriculum.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".