Digital storytelling and intersectional identities: youth with refugee experiences (Re)claiming life stories
Bibliographic record
Abstract
This study addresses the urgent need to develop pedagogies that enhance language and literacy learning and identity affirmation (and resistance) opportunities for youth from refugee backgrounds. In Canadian high schools, this population of students enter school with varying levels of literacy in their first language(s), as well as potentially difficult experiences due to their forced migration. For many, language-content learning may become a formidable challenge, negatively impacting identities. A growing corpus of case studies is beginning to show how pedagogies that draw on youth’s everyday meaning making and their intersectional identities can effectively engage refugee-background learners in academic learning. In this qualitative case study involving nine refugee-background youth in an English language learning classroom in Western Canada, we explore the potential for digital storytelling to enable learners to draw from their full communicative repertoires to enhance language and literacy learning and enable intersectional identity representation, renegotiation and affirmation. Our study is informed by three interrelated conceptual frameworks: intersectionality , communicative repertoires , and digital literacies . We undertook a six-phase process for thematic data analysis of the collection of digital stories, using contextual data such as semi-structured interviews, participant observations and informal conversations to understand the relationship between the students’ digital stories and their lived experiences. Using an adaptation of Rose’s (2016) visual methodology, we provide a more in-depth analysis of the digital stories of two students with a focus on the intersectionality of the various contexts of their lives and identities. Across all participants, four interweaving themes emerged from our thematic analysis of the nine digital stories overall: 1) contesting single-axis labels; 2) reconciling mutually constitutive categories of difference; 3) (re)claiming life stories; and 4) recognizing the critical role of audience. (293)
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.012 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".