Territorial and Digital Borders and Migrant Vulnerability Under a Pandemic Crisis
Bibliographic record
Abstract
Abstract People on the move are often left out of conversations around technological development and become guinea pigs for testing new surveillance tools before bringing them to the wider population. These experiments range from big data predictions about population movements in humanitarian crises to automated decision-making in immigration and refugee applications to AI lie detectors at European airports. The Covid-19 pandemic has seen an increase of technological solutions presented as viable ways to stop its spread. Governments’ move toward biosurveillance has increased tracking, automated drones, and other technologies that purport to manage migration. However, refugees and people crossing borders are disproportionately targeted, with far-reaching impacts on various human rights. Drawing on interviews with affected communities in Belgium and Greece in 2020, this chapter explores how technological experiments on refugees are often discriminatory, breach privacy, and endanger lives. Lack of regulation of such technological experimentation and a pre-existing opaque decision-making ecosystem creates a governance gap that leaves room for far-reaching human rights impacts in this time of exception, with private sector interest setting the agenda. Blanket technological solutions do not address the root causes of displacement, forced migration, and economic inequality – all factors exacerbating the vulnerabilities communities on the move face in these pandemic times.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".