Bordering Vulnerability: Media Portrayal of Climate Migrants in Quebec’s Visual Narrative
Bibliographic record
Abstract
How are migrants and refugees represented in media visual discourse in Quebec? This article explores the representation of migrants and refugees, particularly climate migrants, in Quebec media visual representations. Climate migrants, often perceived as “simple” migrants or as refugees due to a hitherto incomplete international legal definition, are faced with increasingly frequent, urgent, and irreversible displacements. Their vulnerability is exacerbated by the intensification of border restrictions, repulsion based on national security, and political and media discourses. In this context, we specifically focus on the precarious situation of climate migrants at the land border between Canada and the United States, particularly at “regular” and “irregular” entry points in Quebec. In exploratory research, this paper examines the media visual portrayal of these individuals and of topics related to them. We focus on a visual analysis of key Quebec written media between 2018 and 2023. It aims to identify the types of content presented and the key actors involved, analyze the images compositions, elements, and symbols to understand the messages they convey and interpret these messages in the context of the target audience and the media environment. Additionally, it discusses the use of visual rhetoric and its impact on the perception of migration issues. This study contributes to a better understanding of how migrants are portrayed, and the implications of such portrayals for public perception and attitudes toward migration.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".