Bibliographic record
Abstract
In the context of this article, we propose to think about the concept of tolerance as part of the communication model of law. In fact, it is a question of seeing what the requirements of tolerance are as an integral part of the concept of law itself. Since tolerance, like law, presupposes reciprocity with regard to human dignity, we will also try to see how it manifests itself in law. In fact, on what basis can law be tolerant and in what horizon must it work to do so? The concept of law that today allows us to think adequately about tolerance as an integral part of law seems to us to be the communication model of law developed by the philosopher Jürgen Habermas. The communication model favors an open and thoughtful way of thinking about law, with a political conception of tolerance as a place of encounter and recognition that is situated in the democratic process where the author of laws and rights must bear the burden of encountering others in dialogue.We want to think about tolerance in law in relation to what is surely the most important challenge at the end of this century, namely the awakening of identity in democratic and pluralistic societies. This awakening of identity is a guiding way of positioning our discourse on tolerance in law. It is about thinking about our tolerance towards others, towards what they are or would like to be. However, we will not develop the issue of identity itself.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.047 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".