Refracting exoticism in video representations of the victim-refugee: K’Naan, Angelina Jolie and research responsibilities
Bibliographic record
Abstract
Media that makes use of the fixed, two-dimensional victim-refugee figure participates in a kind of exoticization of refugee-ed people by reading forced displacement exclusively through the lens of suffering. Yet the body of scholarship critiquing this media is susceptible to saying more about the scholars and their concerns than about the concerns of those whose experiences are being represented. This article returns to focus group research from 2009 when the author ran media discussion workshops with refugee activists, including both refugee and citizen participants. The workshop discussions focused on K’Naan’s hip hop video ‘Soobax’ and Hollywood film Beyond Borders. The research aimed to understand the pedagogical potential of textual and audio-visual narrations of refugee cultures but became an exercise in refracting the exoticization latent in the project’s research questions. One important outcome of this research was the different emphases in participant responses to the victim-refugee figure in the videos. Workshop participants with a refugee background iterated that, given the context of growing apathy and antipathy towards refugee claimants in Canada, the representation of refugees as suffering victims remains a useful and powerful intervention in public debates. The article finishes with some reflections on the implications of the research findings and on the responsibility of engaged scholarship for researchers in cultural refugee studies and humanitarian communication.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".