Bibliographic record
Abstract
This essay tries to shed light on a central, yet curiously understudied, concept in modern legal and political discourse: the sense of justice. It has been said that "law in the last analysis must reflect the general community sense of justice." But what is it? What do people mean when they refer to the sense justice? To assemble a serviceable account of the sense of justice requires leaving the comfortable confines of American jurisprudence, which has contributed precious little to this subject, and instead taking an interdisciplinary approach. Moral psychology, for one, has explored notions of moral sentiment and empathy for centuries. Political theory, too, deserves our attention, mainly because Rawls assigned the sense of justice a pivotal, though generally underappreciated, role in his theory of justice. Even linguistics will get a closer look because of the intriguing parallels between a sense of justice and a sense of language, and of moral and linguistic competence. In the end, the sense of justice emerges as a general normative competence consisting of a bundle of cognitive and volitional capacities. Understood in this way, the sense of justice is nothing less than a precondition of just cooperation and stability in a modern world void of substantive commonalities.
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 | 0.011 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.055 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".