The second generation of human security: lessons from the UN and EU experience
Bibliographic record
Abstract
The concept of human security, while much contested in both academic and policy debates, and highly fragmented across different meanings and forms of implementation, offers a potential locus around which global security discourse might converge, particularly in light of current shifts in US security thinking. However, key pioneers of human security, such as the United Nations and Canada, appear to be losing their enthusiasm for the concept, just at the moment when others such as the European Union, are advancing a human security agenda. This article examines the divergence of human security narratives between the UN and the EU. It argues that the UN's use of the concept ran aground owing to a triple problematic of lack of clarity, confusion between previously distinct policy streams on human rights and human development and conceptual overstretch. After assessing the EU experience with the concept to date, the article argues that future use of human security will require greater focus on how it deepens ideas of individual security, rather than treating it as an agenda for broadening security. As well as a need to project clarity on the conceptual definition of human security, there is also a need to associate human security with greater clarity of intent. If successful, this would contribute to establishing second generation human security as a new policy paradigm.
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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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".