Teaching appropriate conflict resolution methods as an incentive to de-judicialization
Bibliographic record
Abstract
The multi-door justice system is an alternative for reducing the number of lawsuits brought before the courts. The so-called crisis of the Judiciary can be alleviated through the use by society of other appropriate methods of conflict resolution, such as negotiation, conciliation, mediation and arbitration. However, in a society characterized by a culture of litigation, the judicialization of social conflicts is also a cultural problem, since citizens commonly only know the judicial route as a mechanism for social pacification. This raises the question of whether ignorance of the multi-door justice system is, in fact, an aggravating factor in the phenomenon of judicialization, so that, contrario sensu, teaching other ways of resolving disputes could be considered a cultural strategy to achieve gradual de-judicialization. Therefore, based on a review of the literature, especially consultations of specialized doctrine and analysis of legal texts, this article presents the theory of conflict and the way in which disputes occur in society, as well as explaining what the so-called multi-door justice system consists of, briefly presenting the appropriate dispute resolution methods that make it up. The aim was also to demonstrate that judicialization is based on a cultural problem, which must be gradually resolved by teaching the techniques for reaching appropriate solutions to the disputes that emerge from social life.
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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 | 0.017 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".