Ease of Inferencing, Learner Inferential Strategies, and Their Relationship with the Retention of Word Meanings Inferred from Context
Bibliographic record
Abstract
In recent years the study of second language (L2) vocabulary learning through reading has attracted much attention in the field of L2 acquisition. A specific area that has received wide interest is the examination of the processes involved in deriving word meanings from context. The purpose of the present study was to examine the relationships between the ease with which learners infer word meanings from context, the inferential strategies they use, and their subsequent retention of these words. Eleven ESL learners read and inferred the meanings of 10 unknown words in an academic text. Think-aloud procedures were used to collect data about learners’ inferential strategies and their correct inferences during reading. A pre-test and a post-test were used to examine learners’ degrees of retention. The results showed an inverse relationship between ease of inference and retention. Quantitative and qualitative analyses of learners’ inferential strategies showed a significant relationship between the type and frequency of use of inferential strategies and retention. The findings confirm that a distinction needs to be made between ease of inferencing and the retention of the word meanings inferred from the context. Findings also suggest that the degree of retention depends on the type of strategies used.
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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".