Bibliographic record
Abstract
The lawers of antiquity defined justice as "giving to each what is his ius [due, right]": ius suum cuique tribuere. Until or unless someone can rightfully claim "that is owed to me [him, or them]" there is no issue of justice. For any practical purpose, the discourse of rights depends on our ability to recognize with some precision who owes what to whom. Bills and charters of rights typically enumerate things which the government owes to citizens or persons. Since World War Two, domestic and international declarations have emphasized obligations of states to recognize human or natural rights. However, these lists often include "rights" which are rather general and under-specified. Under-specified rights have two deleterious consequences for constitutionally limited governments. First, such "rights" inspire the belief that persons have rights prior to anyone knowing precisely what they are. Second, under-specified rights typically burden courts with the task of discovering on a case by case basis the precise nature of the right under dispute. Since bills or charters of rights aim to limit the government, we might doubt whether this purpose is really achieved when the government must specify the right on an ad hoc basis. These problems are investigated in light of U.S. constitutional history.
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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.051 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.009 | 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".