Bibliographic record
Abstract
The Covid-19 pandemic has exposed a turbulent world of inequality, failing states, crime, violence, racism and authoritarianism. But it has also opened up the practical possibilities of human security – the notion that governments and international institutions take responsibility for the wellbeing of individuals and the communities in which they live, protecting them from global ills such as Covid-19 and ensuring both material security (safety from poverty and deprivation) and physical security (safety from violence and crime). My focus on this essay is on physical security, and, in particular, how to address the problems that contemporary war inflicts upon individuals and communities. Of course, physical and material security are intimately connected. Poverty, inequality, and deprivation are undoubtedly a cause of violence and crime and, by the same token, violence accentuates precarity. But while solving the problems of material redistribution could well reduce the incentives for violence, this is extremely difficult to achieve in violent contexts where the warring parties control the flow of resources. Thus, finding ways to mitigate violence is often a precondition for material security. In this essay, I outline an understanding of human security as a tool for reducing violent conflict.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".