Media and Construction of Difference: How Media Representations Work to Criminalize, Label, and Induce Border-Restrictions against Young African Female Migrants in Europe
Bibliographic record
Abstract
The United Nation’s Technical Specialist for Adolescents and Youth at the UN Population Fund, Sylvia Wong reveals that young adults currently represent the largest proportion of transnational migrants (Wong, 2009). Migrant youth, and in this context, female African migrants are being subjected to very difficult transit experiences both at transnational borders (Toasije, 2009; Brachet, 2012) and in the receiving societies (Ki-moon, 2009; Solimano, 2010). Our work disturbs existing notion of free movement of individuals across transnational borders to accentuate the effect of Western media representations on Europe-bound female youth migrants from Sub-Saharan Africa. We foreground the pervasive border-restrictions, oppressive treatments, and involuntary deportations experienced by these female migrants in neo-colonial surveillance systems, and tropes of racist securitization masking the interlocking systems of oppression the female migrants have to deal with. The work uses textual analysis to speak to earlier research-report from regimented qualitative field-study conducted in 2013 in the Republic of Malta. It argues that labeling and criminalization of young African female migrants by the European media results in negative public opinions, and subsequently, severe restrictive and oppressive practices against these migrants both at EU borders and in the host societies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.015 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| 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".