Writing and Identity: A Narrative Inquiry on Two Saudi Arabian ESL Females
Bibliographic record
Abstract
Writing in a second language is considered extremely challenging for several reasons. Concerns that perplex second-language learners include cognitive complications, the composing process, building arguments, and constructing an identity as a writer. Cultural issues related to writing also pose problems for second-language writers This paper focuses exclusively on how international students, female Saudi ESL students, construct their writing identity in the ESL milieu and navigate critical issues in cross-cultural writing. This paper explores how two ESL Saudi Arabian female students in an English program in the United States negotiate and construct their identities while writing in English. The study will also investigate challenges faced by those students in acquiring English writing skills and how those challenges inform their thinking and shape or reshape their identities as writers. The study involves two female Saudi students who are studying the English language at a mid-size diverse Southwest public university in the United States. The researcher collected the data through semi-structured interviews with the participants and then performed a textual analysis of their responses. The researcher transcribed and analyzed the data and describes the results thematically herein. The findings of this study augment our understanding in how female Saudi ESL students construct their identities as writers. The analysis covers some sociocultural factors that shape their writing. The paper concludes with pedagogical implications for ESL teachers and suggestions for future study.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".