Agents, Structures and Evil in World Politics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concept of evil adds complexity to our moral analysis and judgement of social and political phenomena, but the language of evil can be abused, either to exclude persons or groups from our universes of moral obligation, or to subvert fragile international and domestic moral orders and the conditions for human moral agency and responsibility. Despite these dangers the concept of evil is indispensable for identifying acts and states of affairs that violate our most basic moral ideals and expectations. Recognition of evil leads to three distinct but interrelated questions: who is to blame? How could such evil happen? And how can it be prevented from recurring? Answering these questions requires an account of agents, structures and their relationship. Acknowledging that agents and structures are mutually constituted need not absolve agents of moral responsibility; rather, it is vital to refining judgements of moral responsibility, and understanding how various social and political evils occur, as well as how to prevent their future recurrence.
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.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it