Artificial Intelligence in Border Management Devices: A Multiple Correspondence Analysis of European Union Funding provided through the Horizon 2020 Program
Bibliographic record
Abstract
This thesis answers three questions: 1) What were the most common features of devices developed during the European Union’s Horizon 2020 (H2020) research framework?; 2) Can actors/industries be associated with specific project features?; and 3) Given the emerging use of Artificial Intelligence (AI) in relation to border security devices, is the use of AI (and/or the use of certain types of AI) associated with certain features, and if so, which ones? To answer these questions this thesis uses Multiple Correspondence Analysis to analyze 42 H2020 projects which produced a border security device. These results of this show, in agreement with other literature, that projects largely conform to three clusters, those which: 1) observe territory, 2) control the flow of goods/people, and 3) protect infrastructure. Moreover, it shows that certain industries/actors can be associated with specific features and the use of AI is attributed most to projects in the first cluster.
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.031 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".