Extensive Reading as a Means of Vocabulary Development amongst English Language Learners in Nigeria: Consolidating on Knowledge
Bibliographic record
Abstract
Mastery and fluency in a language interestingly, requires vocabulary development which entails learning of multiple active and passive vocabularies in that language. Language is built on words which are essential for communication with symbols of meaning. Reading is a receptive linguistic process that involves interpretation of written symbols which are meaning preserving. In our contemporary times, the act of reading is gradually going extinct, because of obvious reasons – advancements in Science and Technology which has given rise to computer-assisted learning, social media support in information dissemination, high cost of publishing, time constraints and lack of interest in reading, especially amongst youths who prefer accessing information through social media. However, a common saying adjudges great readers to great minds with great experiences, akin to an extensive traveler. This paper ascertains the importance of extensive reading in vocabulary development for academic success, with positive implications for language learners of English.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".