Humanitarian and Human Rights Surveillance: The Challenge to Border Surveillance and Invisibility?
Bibliographic record
Abstract
The European border regime has traditionally rested on the hidden surveillance activities of border authorities, which have contributed to human rights violations (including “push-back” and “left-to-die” practices) and a rising migrant death toll. Recently a number of humanitarian and activist organizations, including Migrant Offshore Aid Station (MOAS), Médecins Sans Frontières (MSF), Sea-Watch, and WatchTheMed, have organized to aid migrants in distress at sea using surveillance technologies, ranging from drones to GPS. By doing so, they presented a challenge to the European border surveillance regime. In dialogue with the concept of countersurveillance, this paper introduces the concepts of humanitarian surveillance and human rights surveillance and deploys them to examine and categorize the activities of MOAS, MSF, Sea-Watch, and WatchTheMed. Humanitarian surveillance narrowly focuses on aiding victims of surveillance without problematizing the logic and hierarchies of surveillance, while human rights surveillance operates as a form of countersurveillance; it aims to protect and advance the human rights of victims of surveillance and expose human rights violations committed by authorities through opposing the hierarchies of surveillance. The paper shows how civilian groups incorporate elements of humanitarian and human rights surveillance in their activities at varying levels and discusses the extent to which they challenge the European border surveillance regime.
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 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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.014 | 0.022 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".