Bibliographic record
Abstract
Although much research on English for specific purposes (ESP) has been conducted, little focus has been placed on investigating the mechanisms involved in ESP readers’ comprehension process. This case study explored the reading process that characterizes a competent ESP reader’s comprehension. A juris doctor student attending a major university in the United States was selected for the case study. Qualitative data were gathered through a semi-structured interview, recall protocol, and document analysis, and were thematically coded. The findings revealed the following: (a) The professional domain determines the major purposes of ESP reading. (b) Institutional demands and inadequate L2 proficiency within the target discourse prevent background knowledge in the first language (L1) from being transferred to L2 texts are the major sources of comprehension difficulty. (c) Three strategies can be used to overcome such difficulties. First, schemata (background knowledge) in L1 can facilitate comprehension and enhance the reading process. Second, genre knowledge learned in L1 can be used to dissect the information structure of L2 texts. Finally, the use of L1-based materials can help to overcome the comprehension difficulties arising from the L2 material. The implications for L2 reading instruction and future ESP reading research are discussed.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".