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Record W4382678163 · doi:10.1080/08865655.2023.2229838

Externalized Within, Everyday Bordering Processes Affecting Undocumented Moroccans in the Borderlands of Ceuta and Melilla, Spain

2023· article· en· W4382678163 on OpenAlexvenueno aff
Nina Sahraoui

Bibliographic record

VenueJournal of Borderlands Studies · 2023
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsnot available
FundersHORIZON EUROPE European Research Council
KeywordsContext (archaeology)SociologyPoliticsPolitical scienceGender studiesGeographyLaw

Abstract

fetched live from OpenAlex

This article proposes the notion of ‘externalized within’ to trace the implications of Ceuta and Melilla becoming frontlines of Fortress Europe in the context of entrenched postcolonial hierarchies. While building on previous contributions around ‘internal externalization’, this article goes beyond the policy analogy that led to the coining of this phrase in order to focus on the social implications of this phenomenon in terms of everyday bordering. This article explores how exclusionary policies specific to Spanish borderlands impede undocumented migrants from accessing social services through concrete legal, economic and social barriers, leading them to become ‘externalized within’. The cases of children’s admission to school and pregnant women’s access to healthcare are highly symbolic issues, as these groups tend to be portrayed as vulnerable and tend to benefit from some forms of inclusion both in mainland Spain and many other European settings. In the context of Ceuta and Melilla, the marginalisation of undocumented Moroccans is intensified by the divisive effects of border fortification, to be seen in the weaponizing of basic social services. The article relies on qualitative fieldwork conducted with undocumented Moroccan women, NGO members, social workers and healthcare professionals and managers in 2016 and 2017.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.040
GPT teacher head0.376
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2023
Admission routes1
Has abstractyes

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