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Record W3197594728 · doi:10.5539/elt.v14n9p80

Second-Language Reading Process for Professional English: A Case Study

2021· article· en· W3197594728 on OpenAlexvenueno aff
Che-Han Chen

Bibliographic record

VenueEnglish Language Teaching · 2021
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsnot available
Fundersnot available
KeywordsReading comprehensionReading (process)ComprehensionPsychologyEnglish for specific purposesProtocol analysisLinguisticsProcess (computing)RecallQualitative researchLanguage proficiencyMathematics educationComputer scienceCognitive psychologySociologyCognitive science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.022
GPT teacher head0.304
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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