Representation of Voice: A Narrative Inquiry of Indonesian EFL Learners in Poetry Writing Experience
Bibliographic record
Abstract
This study was conducted to identify the students' voices and the challenges in writing the poetry from the narratives behind their poetry writing. Since the primary source of the study data was the students' narratives, this study was conducted by following the narrative inquiry method. Fifteen EFL learners who took poetry classes were taken as the study samples. The study data were collected from the students' poems, journals, and interviews. Those three different methods were applied to ensure the data validity and reliability. To identify the voices, the researchers interpreted the voices from the dictions that the students chose to write the poetry. Then, the researchers confirmed the voice's interpretation by comparing them with the students' journals and interview results. To identify the challenges, the researchers qualitatively analyzed the students' journals and the interview results using cross-case analysis. This study found that behind the narratives of learners, there are voices that have been unheard for years, the unspoken words that are kept for themselves. The voices are trauma, anxiety, and hope. The trauma includes the trauma of paranoia, bullying, and past life, and the anxiety includes anxiety of the past, present, and future. Meanwhile, the voice of hope covers optimism and enthusiasm. Besides, this study also identified that the students found some challenges in writing poetry, and they overcame those challenges by practicing more, reading more literature, finding new words, accepting more information, being more flexible, and being open to new contexts.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.004 |
| 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".