Bibliographic record
Abstract
Despite increasing attention to images in the study of world politics, the role of visual representations in transnational governance processes in general and migration governance, in particular, has received less attention. This paper aims to fill this gap by examining the visual representation of migration governance by the two Canadian government institutions responsible for it: Immigration, Refugees and Citizenship Canada (IRCC) and the Canada Border Services Agency (CBSA). Through a multi-modal analysis of their X (formerly Twitter) images, the paper shows how they tend to shy away from a visual representation of people on the move, privileging a technical communication in an aspiration toward a “neutral” representation of migration issues. Secondly, it sheds light on the discrepancies between the policies directed toward Afghani and Ukrainian refugees and the unexpectedly undifferentiated visual communication about the two groups. Finally, the paper explores what the pictures accompanying X (formerly Twitter) posts of IRCC and CBSA can tell about how these government institutions represent themselves. Findings showed that government institutions’ esthetic practices are not always in line with the institutional migration policies and text narratives, suggesting that different government implementation tools (i.e., regulations, practices, textual discourses) may not reinforce each other but advance alternative perspectives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".