Reconsidering “community”: a normative model to address communities in the law
Bibliographic record
Abstract
This article proposes a normative infrastructure concerning the legal treatment of communities in the law. This normative infrastructure, based on the state’s pluralistic obligation to protect the existence of multiple, diverse conceptions of good within a society, first requires the state to distinguish between different communities according to their characteristics to determine how each should be treated. This article suggests three key elements for distinguishing communities in this way: the social compatibility of the community with other communities and with society as a whole, the role that cooperation plays in the realization of the community’s conception of the good life, and the community’s political and economic strength.We then demonstrate how the proposed normative infrastructure should apply to various legal branches including private law (focusing on proprietary arrangements of residential communities and relational contracts); public law (aiming to provide a comprehensive policy to regulate the relationship between the liberal state and illiberal communities) and labor law (proposing a novel framework for rights and duties in workplaces).
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 | 0.029 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.019 | 0.097 |
| Scholarly communication | 0.018 | 0.025 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.018 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".