Bibliographic record
Abstract
Abstract Scholars have explored the relationship between punishment and social deprivation—including poverty, unemployment, low-education levels, and family disruption—from different perspectives. Some contend that social deprivation should mitigate culpability, decrease sentence severity based on compassion, or excuse wrongdoing. Others posit that social deprivation undermines the state’s standing to call individuals to account for wrongdoing. This chapter advances a different argument. Building on Kantian theories of legal authority, republicanism, and fiduciary theory, this chapter contends that the state has a moral duty to alleviate social disadvantage to legitimize coercion and punishment. It demonstrates why the state must create a system of criminalization and punishment to prevent certain forms of domination. Yet the state fails to accommodate the reality of social deprivation within this system, which undermines its legitimacy and subjects socially deprived persons to domination. The concluding part of this chapter shows why the state has a corrective fiduciary duty to address social deprivation. The fulfillment of this duty helps legitimize coercion and punishment, and counteracts domination associated with the criminal justice system.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".