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Record W31009077 · doi:10.1111/nph.15867

Dark Side of the Rock : Borders, Exceptionalism, and the Precarious Case of Ceuta and Melilla

2012· article· en· W31009077 on OpenAlexaff
Can E. Mutlu, Christopher C. Leite

Bibliographic record

VenueEurasia Border Review · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsUniversity of Ottawa
FundersBasic Energy SciencesOffice of Science
KeywordsSituatedExceptionalismContext (archaeology)Border crossingGeographySubject (documents)Economic geographyObject (grammar)ImmigrationPolitical scienceLawComputer scienceArchaeologyPolitics

Abstract

fetched live from OpenAlex

The Schengen zone creates two kinds of subjects: regular and irregular, and two kinds of borders: open and closed. For the irregular migrant, the border becomes a mobility security assemblage consisting of fences, towers, guards, cameras, and sensors, whereas for the regular traveller the border consists of an immigration counter and a rubber stamp on the passport. The object location, along with the historical and geographical context of specific border crossings, define the security practices at that border based on the subject of separation, and the function and location of the border determine the nature of a border-crossing experience. As such, the specific histories of the different border crossings play a central role in determining border management practices. As such, we argue that rather than a uniform European mobility experience, there are situated intersubjectivities of border crossings differing from one port of entry to another. This article argues that Ceuta and Melilla present a window into the role of petty-sovereigns in determining these situated intersubjectivities that define different mobility security regimes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.017
GPT teacher head0.352
Teacher spread0.335 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations9
Published2012
Admission routes1
Has abstractyes

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