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Record W4405656658 · doi:10.18357/bigr52202421666

Temporalities in 3D: Speeds, Intersections, and Time Sequentialities at the Portuguese Border

2024· article· en· W4405656658 on OpenAlexvenueno aff
Mafalda Carapeto

Bibliographic record

VenueBorders in Globalization Review · 2024
Typearticle
Languageen
FieldEngineering
TopicTraffic Prediction and Management Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsTemporalitiesPortugueseHistoryGeographyComputer sciencePolitical scienceLinguisticsLaw

Abstract

fetched live from OpenAlex

This article addresses the Portuguese border control regime by looking into the relationship dynamics between inspectors and foreign citizens at the first line of inspection. Through the lens of temporality, I consider how the presence or absence of certain bureaucratic records presented by travellers functions as a control device that produces three temporal dimensions which intersect with each other during the check, as exercised by inspectors. The way in which certain documents result in different speeds of document control (microtemporalities—advances, retreats, and hesitation); subsequently, I reflect on the elasticity of time, looking at the intersection between the past, present, and future; finally, I analyse how inspectors shift their gaze from the documents to the details they are composed of, thus introducing a sequential dimension to their assessment. This article argues that the uncertainty experienced by travellers reflects the instability and inconsistency of the state, caused by the contingency that permeates their encounters at the border where time operates as a technique of power. The study is based on 11 months of ethnographic fieldwork conducted in 2021 and 2022, centred on the daily life of the inspectors of the Portuguese Immigration and Borders Service at an airport in mainland Portugal. Keywords: anthropology of the state; external border; temporalities of migration; control devices.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.163
Threshold uncertainty score0.404

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.276
Teacher spread0.268 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

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