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Record W4404924385 · doi:10.7202/1114788ar

Mettre les frontières en nombre : éléments de genèse de la quantification de l’irrégularité aux frontières de l’Union européenne

2024· article· fr· W4404924385 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCriminologie · 2024
Typearticle
Languagefr
FieldSocial Sciences
TopicEuropean Criminal Justice and Data Protection
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical science

Abstract

fetched live from OpenAlex

En l’espace d’une quinzaine d’années, entre le début des années 1990 et le milieu des années 2000, un réseau d’acteurs opérant au sein des institutions de l’Union européenne va construire un dispositif transnational visant à mettre en nombre, mesurer et comparer différents aspects de l’irrégularité aux frontières des États membres. L’article présente une enquête historique inédite de ce dispositif, étudié à partir d’un de ses points d’émergence, le Centre d’information, de réflexion et d’échanges en matière de franchissement des frontières et d’immigration (CIREFI), créé en 1992 et démantelé en 2010 après sa fusion avec l’agence Frontex. Alors que la construction statistique de l’irrégularité n’a que peu retenu l’attention des études sur les frontières, cette recherche révèle les différentes opérations qui permettent la production de ces statistiques grâce à l’analyse d’un corpus d’archives au travers d’outils théoriques et méthodologiques issus de la sociohistoire de la quantification et des études criminologiques.

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.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.805
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.218
GPT teacher head0.404
Teacher spread0.186 · 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