Bibliographic record
Abstract
Contemporary cross-border migration patterns often reflect historical colonial ideologies, with inequalities in freedom of movement closely tied to national identity, race, ethnicity, and gender. Representations of migrant women in news outlets frequently aim to symbolically position them as passive victims and depoliticized subjects dependent on external aid. While various facets of migration have been explored in critical media scholarship, more attention needs to be given to photography's role in depicting women's movement across geographical and cultural borders beyond traditional photojournalistic and humanitarian conventions. Some migrant women photographers, such as Maryam Wahid and Mitra Samavaki, use visual practices to contest the inadequacy and lack of representation of migrant women and subjective migration experiences. These photographers invoke metaphorical visual languages to position women in front of and behind the camera as narrators of their own stories. Considering this context, this paper explores the potential of self-authored visual narratives—created and circulated by migrant women photographers—to offer innovative insights into subjects' agency. I discuss these alternative representations, building on Ariella Azoulay’s new political ontology of photography, T. J. Demos’ approach to aesthetics of migration, and Anne Ring Petersen's postmigrant perspective. This study theorizes the relationship between migration, photography, and first-person perspectives by examining how the political is reinstituted into an aesthetic framework within photographic practices. In conclusion, these artists portray mnemonic and subjective aspects of their migration experiences, engaging in image-making to reflect on a world shaped by and through migration. These self-authored visual narratives transcend the codes of objectivity and humanitarian traditions by creating a political space for transforming, contesting, and negotiating aesthetic complexities associated with memory, self, and womanhood.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".