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Record W4391693535 · doi:10.55905/revconv.17n.2-048

Teaching appropriate conflict resolution methods as an incentive to de-judicialization

2024· article· en· W4391693535 on OpenAlexaff
José Bruno Martins Leão

Bibliographic record

VenueContribuciones a las Ciencias Sociales · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDispute Resolution and Class Actions
Canadian institutionsMinistère de l’Emploi et de la Solidarité Sociale (Québec)
Fundersnot available
KeywordsIncentiveConflict resolutionResolution (logic)Political scienceComputer scienceEconomicsLawMicroeconomicsArtificial intelligence

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.056
GPT teacher head0.369
Teacher spread0.313 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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