An Investigation of the Possible Effects of Favored Contexts in Second Language Vocabulary Acquisition
Bibliographic record
Abstract
It is widely agreed that much second language vocabulary learning occurs while the learner is attentively engaged in the process of reading and interpreting the texts encountered. It is also argued that to understand vocabulary learning linguists cannot limit the investigation of the process of vocabulary learning to word meaning (Gass, 1999). Since many questions remain unsettled in this domain, this study was conducted in the context of TEFL to investigate the effect of teaching vocabulary through its use in contexts in which learners have an interest versus contexts in which they are not that much interested. It, therefore, examines the impact(s) of contextual vocabulary learning and attempts to have a comparison of the effect(s) of the role of favored contexts as opposed to disfavored contexts. The experiment involved two groups of twenty-five male Iranian participants aged from 15 to 25 at the intermediate level in an Iranian English language institute. All things considered, a pretest-posttest control group design was determined to check the accuracy of the researchers’ hypothesis in a short-term treatment through the application of reading comprehension tests (RCT). As a result, the overall findings support the initial idea that second language vocabulary acquisition (SLVA) is better achieved through the use of favored-contexts.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".