UN GRAIN DE SABLE DANS L’ENGRENAGE DU SYSTÈME JURIDIQUE. LES JUSTICIABLES NON REPRÉSENTÉS: PROBLÈMES OU SYMPTÔMES?
Bibliographic record
Abstract
The number of self-represented litigants [SRL] that are trying to defend by themselves their rights are increasing constantly, even in the highest tribunals. This situation is not without consequences for both the actors of the legal system and the SRL themselves. In the last years, many initiatives have been taken in order to contain the negative effects of the presence of SRL in the system. In this article, the authors are analysing the discourse coming from the legal doctrine on the phenomenon of self-representation. First of all, they find that SRL are generally held as the only responsible for the annoying consequences provoked by their very presence. Secondly, they demonstrate that the majority of the solutions submitted in the literature are meant to maintain the normal functioning of the legal system, without submitting any holistic or systematic solutions. Finally, they consider the possibility that it would be the paradigmatic basis of the legal thinking that is the principal barrier for a global assessment of the problematic and for the implementation of efficient solutions.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.014 | 0.054 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| 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".