Etymology and the Development of L2 Vocabulary: The Case of ESL Students at the University of Botswana
Bibliographic record
Abstract
Part of the history of English is that many of its words are of Graeco-Latinate origin. Hence, the vocabulary of the language comprises words which are short and familiar and those which are foreign and long (Quirk,1978, p. 138). However, both L1 and L2 users have to get acquainted with the second group, which dominates academic discourse. Our students are disadvantaged on two grounds. Firstly, vocabulary instruction for them seems quite deficient in scope and depth, and secondly, the students tend to acquire the vocabulary of academic discourse necessary for their success in tandem with the learning of concepts which come incased in words they are unfamiliar with. Our paper uses data collected from the students’ writing over a period of twenty years to examine their specific problems relating to the etymology of English words. Two questions are addressed: What problems does the etymology of English words pose for ESL students? What measures can be adopted to alleviate these problems? We discuss confused pairs of words, pairs erroneously considered synonymous, and coinage resulting from student’s lack of appropriate vocabulary. We recommend that teaching the etymology of words used in academic discourse would assist our students to improve their fluency in English.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".