On the concepts of human security, dignity and vulnerability: understanding the mechanisms of being ‘at risk’
Bibliographic record
Abstract
What does it mean to be ‘at risk’? How do inherent characteristics, contextual factors, and relationships intertwine to increase susceptibility to harm? Vulnerability has intrigued scholars across sociology, politics, and human rights law. However, using ‘vulnerability’ to describe people’s situations has faced criticism for potentially essentialising, oversimplifying, or disempowering individuals, which leads to their instrumentalisation. Alternatives emphasising empowerment, resilience, and intersectionality have surfaced to address these critiques, focusing on contextual threats to human security and dignity. Inherent traits may influence susceptibility to harm, while contextual factors consider the subject, environment, and timeframe through tangible and intangible elements. This paper aims to explore the mechanisms of vulnerability comprehensively, incorporating inherent factors, contextual elements, and relational dynamics. By proposing a conceptual framework to categorise vulnerability factors, this study offers a practical approach to address gaps in international human rights law, particularly useful to better understand emerging issues like climate migration and compounded discrimination. Ultimately, the paper calls for adaptive, interdisciplinary, and inclusive reforms of human rights mechanisms to safeguard dignity and equity in the face of global challenges.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".