Welcome to Europe! Linking the EU Parliament LUX Film Prize and the impact of migration films to the emergence of a European public sphere
Bibliographic record
Abstract
Abstract Migration films represent an emerging genre of film-making that is increasingly influencing European citizens and policy-makers alike. Through them, we are able to deconstruct negative attitudes about migrants and how they fit into an increasingly cosmopolitan and diverse Europe. In 2008, the EU Parliament created the LUX Film Prize, an award that recognizes European films that embody European traditions, values and integration. In 2009, the prize was awarded to French Director Philippe Lioret’s film Welcome, the story of a Kurdish migrant in France hoping to reach Britain by swimming the English Channel. Despite its fictional scope, it was highly politicized and controversial due to its critical approach of French refugee policies, specifically the L622-1 law prohibiting citizens from offering assistance to undocumented migrants. Following the EU Commission’s acknowledgement of the need to further involve citizens in debates on European issues, this article argues that the LUX Prize is an innovative medium to foster debate and discussion on matters of migration within a wider European public sphere. It has also provided a platform for those outside the political landscape to help shape the discourse on migration, thus reinforcing a more inclusive European civic participation on matters directly affecting them as citizens.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".