Digitisation and Sovereignty in Humanitarian Space: Technologies, Territories and Tensions
Bibliographic record
Abstract
Debates are ongoing on the limits of - and possibilities for - sovereignty in the digital era. While most observers spotlight the implications of the Internet, cryptocurrencies, artificial intelligence/machine learning and advanced data analytics for the sovereignty of nation states, a critical yet under examined question concerns what digital innovations mean for authority, power and control in the humanitarian sphere in which different rules, values and expectations are thought to apply. This forum brings together practitioners and scholars to explore both conceptually and empirically how digitisation and datafication in aid are (re)shaping notions of sovereign power in humanitarian space. The forum's contributors challenge established understandings of sovereignty in new forms of digital humanitarian action. Among other focus areas, the forum draws attention to how cyber dependencies threaten international humanitarian organisations' purported digital sovereignty. It also contests the potential of technologies like blockchain to revolutionise notions of sovereignty in humanitarian assistance and hypothesises about the ineluctable parasitic qualities of humanitarian technology. The forum concludes by proposing that digital technologies deployed in migration contexts might be understood as 'sovereignty experiments'. We invite readers from scholarly, policy and practitioner communities alike to engage closely with these critical perspectives on digitisation and sovereignty in humanitarian space.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.100 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".