Representing Childhood and Forced Migration: Narratives of Borders and Belonging in European Screen Content for Children
Bibliographic record
Abstract
This article explores representations of childhood and forced migration within a selection of European screen content for and about children. Based on the findings of a research project that examined the intersections of children’s media, diversity, and forced migration in Europe (www.euroarabchildrensmedia.org), funded by the UK’s Arts and Humanities Research Council, the article highlights different ways in which ideas of borders and belonging are constructed and deconstructed in a selection of films and television programmes that feature children with an immigration background. Drawing on ideas around the “politics of pity” (Arendt), the analysis explores conditions under which narratives of otherness arise when it comes to representing forcibly displaced children within European-produced children’s screen media. It also examines screen media that destabilize borders of “us” and “the other” by emphasizing the agency of children from migration backgrounds, and revealing both the similarities and the differences between European children with immigration backgrounds and White European-born children. It is argued here that, operating according to the notions of living “together-in-difference” (Ang), “narratability” (Chouliaraki and Stolic), and “the struggle for belonging” (Kebede), these representations destabilize narratives of borders and otherness, suggesting that children with a family history of immigration “belong” to European societies in the same ways as White European-born children.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".