Infer the Meaning of Unknown Words by Sheer Guess or by Clues? – An Exploration on the Clue Use in Chinese EFL Learner’s Lexical Inferencing
Bibliographic record
Abstract
Lexical inferencing is refered to as guessing the meaning of an unknown word using available linguistic and other clues. It is a primary lexical processing strategy to tackle unknown words while reading. This study aims to explore the clue use of Chinese EFL learners in inferring the meaning of unknown word in reading. Two types of introspective research methods have been used to achieve the research aim. 55 participants of four levels (tertiary final, tertiary middle, tertiary initial and senior secondary) were asked to read a sample text and infer the meaning of target words through thinking aloud. Additional information of their lexical inferencing was elicited through stimulated recalls. The results show that Chinese EFL learners use a variety of clues in their lexical inferencing. The findings also reveal some discrepancies of clue use across different levels learners in lexical inferencing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".