“I am <scp> <i>THAT</i> </scp> Refugee!” Raising Critical Multilingual Language Awareness Through Spoken Word Poetry with Refugee‐Background Learners
Bibliographic record
Abstract
Transformative learning in education requires pedagogical change to challenge where knowledge is situated and dislodge the unmerited privileges associated with conventional practices of language and emotion in classrooms. Responding to this call, this paper centers the experiences of two learners with refugee backgrounds and explores how a spoken word poetry curriculum implemented in a language classroom raises critical awareness related to language, identity, race/racism, and belonging. Data were collected from course materials, interviews, journals, and artifacts and were analyzed to examine how emotional attachments connect to people and experiences. By positioning students' lives and languages at the center of critical language learning, spoken word poetry cultivated learner agency, and dialogic engagement. Participants disrupted deficit narratives of refugees as illegitimate and reclaimed their right to be heard , seen and felt , first and foremost, as humans. This study makes a pedagogical contribution to literature by highlighting the implications of integrating spoken word poetry into language classrooms and emphasizes the relationship between emotions and language. The findings will be of interest to educators and researchers interested in disrupting conventional language practices that allow students to resist rather than reproduce forms of oppression.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.004 |
| 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".