Bibliographic record
Abstract
Despite the criticisms of Rawlsian justice that I highlighted in Chapter 3, there is a good deal of Rawls’s account of justice that I think civic liberalism ought to take on board. Indeed, in this chapter I shall defend Rawls against some recent critiques and argue that Rawls’s account of the principles that apply to individuals in A Theory of Justice can be usefully applied to the ideal/non-ideal theory distinction. I defend Rawls not for the purposes of salvaging his principled defence of justice. Rather I am to show that dualism, the position that maintains that different principles apply to institutions and individuals, is a more viable approach than monism. And once we consider the reasons for this, one can make a compelling case for shifting from a principle-oriented account of justice towards a virtue-oriented account. Furthermore, by addressing Rawls’s earlier position we see why we must reject the ‘state-centric’ conception of justice envisioned by Rawls’s political conception of justice. So I defend Rawls in the hope that I can convince proponents of his theory that they need to appropriate a virtue-oriented account of justice (one that is at least partially comprehensive) if they hope to salvage Rawls’s account of justice from the critique launched by monists like G. A. Cohen. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.028 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".