Can the Arts Challenge Mainstream Representations of Migration? An Inquiry into the Aga Khan Museum’s Afghanistan My Love Exhibition
Bibliographic record
Abstract
Abstract In the past few years, numerous art initiatives have addressed the subject of migration and sought to voice an alternative to the predominant images diffused in the mainstream media. This article starts from the premise that the role of the arts in challenging dominant narratives of migration is too often taken for granted and argues for the need for a critical examination of the conditions and modalities through which arts can engage with a key societal debate like migration that has become so divisive on a global scale. Drawing on the notion of art worlds established in the sociology of the arts, we argue it is essential to move beyond a romanticized figure of the enlightened creator and consider the embeddedness of art in a complex network of production and diffusion, which greatly influences the nature of the meanings produced and their reception. The article focuses on the “Afghanistan, My Love” exhibition organized by the Aga Khan Museum of Toronto, Canada, in the aftermath of the Taliban’s takeover of Afghanistan in the summer of 2021, which led to a significant surge of media coverage and the start of a dedicated scheme for the resettlement of Afghan nationals who collaborated with the Canadian Government due to Canada’s active involvement in the conflict. By employing a multi-modal methodology that includes textual and visual social semiotic analysis along with key stakeholder interviews, the paper examines under what conditions art can participate in (re)shaping representations of migration. Drawing from this case study, the article proposes to differentiate between “reframing” and “counter-narrating” to understand the distinct modalities through which the arts can engage with and seek to challenge representations of migration. While reframing seeks to implicitly change perceptions by offering complex portrayals of migrant experiences, counter-narrating introduces explicit alternative discourses. Less than intended to shift underlying structural representations, the latter approach seeks to ignite a collective process and generate empathy.
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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.041 | 0.046 |
| Scholarly communication | 0.017 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".