Exploring visual representations by the UNHCR of the experiences of resettled Syrian refugees in Canada
Bibliographic record
Abstract
In the growing literature on the visual representations of refugees used by international organisations, only a few studies have examined the representations used specifically to portray the experiences of resettled refugees in the global North. This study’s objective is to address that gap by analysing the use of specific images by UNHCR Canada to illustrate the resettlement of Syrian refugees in that country, in the context of the government’s initiative to resettle 25000 Syrian refugees between 2015 and 2016. Through a content analysis of the visual representations used online by UNHCR Canada, this study aims to explore the specificities of these representations. Results show that preference seems to be given to certain types of representations of refugees, such as images picturing one individual or a small group of easily identifiable persons, images of women and girls taking care of their families, children and infants, and so on. These tendencies in terms of representations may have various effects, including in fostering specific reactions (compassion, generosity, etc.) in viewers. They also serve to present a particular solution over others for the refugees depicted. The analysis aims to explore those tendencies in representation detected in the selected images and their potential effects on viewers.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".