Temporalities in 3D: Speeds, Intersections, and Time Sequentialities at the Portuguese Border
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".