Bibliographic record
Abstract
This paper examines the visual culture of mediating migration as a “crisis,” arguing that responses to the movement of peoples (especially into the Global North) is animated by two frames: the forensic and the humanitarian. While these appear to be contradictory image regimes—one relying on data, maps and metrics, the other on iconic portraits of human suffering—the paper argues that it is the vacillation between these two poles that creates the dynamic force of the surge of images of “migrant crises.” Using the image of the thaumatrope – a visual tool for tricking the eye into seeing two images at once – this paper explores how processes of mediation train viewers to see images as data and data as images, and thus to be caught within an ideological framework of crisis. It seeks also to provide examples for intervening in the vacillation between humanitarianism and forensics through activist appropriations of migration data.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.052 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| 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".