Exploring Oral History Methodology as a Culturally Responsive Way to Support the Writing Development of Secondary English Language Learners
Bibliographic record
Abstract
Oral history methods have been used extensively in classroom contexts since the late 1960s to promote the study of social studies and history at middle, secondary, and post-secondary levels. Oral history methodology, focusing on English language learners’ personal stories, has received little attention in research or practice. Through an instrumental case study we explored areas of writing development that could be addressed by drawing on oral history methods with secondary English language learners. Sixteen students from 13 different countries participated in topical oral history interviews about their lived immigration and schooling experiences. Students edited and revised their transcripts to produce written narratives for publication in a class book. We found that students more readily focused on revising the content and form of their narratives when treating the transcripts as an initial draft of their writing; the transcript alleviated the cognitive constraints of producing an initial rough draft. In their revisions, students conducted additional research to clarify content and attended specifically to the form of the narrative to ensure that their ideas were clearly communicated. Based on our research findings, we argue that drawing on oral history methods contributes to a culturally responsive pedagogical practice for working with English language learners that validates the students’ lived histories by legitimizing their stories as a content to be studied in the ESL classroom.
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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 | 0.024 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| 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".