Infrastructures of Dis/Connection: Of Drones, Migration, and Digital Care
Bibliographic record
Abstract
Abstract | Migration from sub-Saharan Africa to Northern Europe is imagined and visualized as the movement of human bodies along different territories, eventually traversing the geographically locatable line of the EU border. What this conceptualization of migration, mobility, and the border leaves unacknowledged is that all three are material-virtual phenomena. This paper addresses the infrastructures of mobility that can be traced and analyzed within a migrant route from Niger to Germany. We highlight the need to connect and/or disconnect as strategies of migration and envisage ways to support freedom of movement by bringing aspects of digital care work into the analysis.Résumé | La migration de l’Afrique subsaharienne vers l’Europe du Nord est imaginée et visualisée comme le mouvement des corps sur différents territoires, traversant éventuellement la ligne géographiquement localisable de la frontière de l’UE. Ce que cette conceptualisation de la migration, de la mobilité et de la frontière ne reconnaît pas, c’est que les trois sont des phénomènes matériels et virtuels. Cet article aborde les infrastructures de mobilité qui peuvent remontées et être analysées au sein d’une route migrante allant du Niger à l’Allemagne. Nous soulignons la nécessité de se connecter et/ou de se déconnecter en tant que stratégies de migration et envisageons des moyens d’appuyer la liberté de circulation en intégrant des aspects du travail en soins numériques dans l’analyse.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".