CONFLICT TRANSFORMATION THROUGH DIGITAL MEDIATION: A STUDY OF BRAZIL'S JUDICIARY COURTS
Bibliographic record
Abstract
This study aims to examine the characteristics of online mediation within public systems, focusing on two pivotal elements: the technological progression instigated by the Covid-19 pandemic that led to an almost full digitalisation of Brazil's judicial system and the subsequent implications of an emancipatory conflict transformation. The research engages social constructivist ontological and epistemology of the south as perspectives, thereby conducting an empirical inquiry into the modifications, aspects, and efficacy of mediation across two judicial districts in São Paulo, Brazil, specifically the cities of Piracicaba and Campinas. The paper underscores the role of mediation within the sphere of online dispute resolution (ODR) in Brazil's legal system, serving to enhance access to justice, expedite communicational changes, and address the limitations of digital mediation while ensuring the active involvement of all parties in the proceedings. Furthermore, the study critically assesses and evaluates the use of mediation as a vital tool in national and international legal systems during the digital judiciary age. To achieve this, a comprehensive re-evaluation of the core principles of mediation is undertaken and juxtaposed with the challenges observed in these regional settings, thus unearthing various topics that necessitate further exploration. In summation, this study offers an insightful understanding of the modalities and regional practices of digital communication in mediation, positing it as a trailblazing approach to enhancing the efficacy of conflict resolution practices and furthering social, cultural, and academic transformation.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".