Introduction to special issue “visual governance in migration”
Bibliographic record
Abstract
In today’s public discourse, images have assumed a pivotal role, a significance that has only grown with the increasing prominence of social media platforms. Images possess a unique ability to transcend language barriers, swiftly convey complex ideas and emotions, encapsulate narratives, and elicit strong emotional responses, rendering information more accessible and engaging. In today’s fast-paced world of social media, where concise, visually appealing content reigns supreme, both real and fake images have become the primary currency of online communication. As the saying goes, “a picture is worth a thousand words.” Images have become not just common but often indispensable elements of communication on virtually every topic. Particularly, subjects of global significance have been widely represented through a diverse array of images, whether it’s overcrowded migrant boats, starving polar bears on melting glaciers, or infographics displaying the latest COVID-19 mortality rates, all of which have gone viral.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.267 | 0.084 |
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".