As the Water Grinds the Stone: Comparison of Represented and Self-represented Appellant Populations in the Federal Court of Appeal
Notice bibliographique
Résumé
This article reports a quantitative and statistically reliable population investigation of 552 Federal Court of Appeal proceedings that were appeals by represented and self-represented appellants who, in 2016 or 2017, appealed decisions of the Federal Court or Tax Court of Canada. Appeals by the Crown, non-Crown represented appellants, and self-represented appellants exhibited markedly different frequencies at which appeals were granted, and patterns for how appeals were terminated. Nearly half of Crown appeals were granted, but less than one in twenty self-represented appellants had any degree of success. While 70% of appeals conducted by lawyers completed the appeal process, less than 40% of self-represented appellant proceedings resulted in a full appeal panel hearing. Incomplete appeals by self-represented appellants usually terminated prior to the appeal record stage, and typically were either abandoned or discontinued. The time required to complete appeals for represented and self-represented appellants is similar. The high observed frequency of problematic litigation records for self-represented appellants supports the hypothesis that a “Distillation Effect” is concentrating abusive litigants in appellate forums.
 High resolution investigation of self-represented appellant subgroups revealed differences within the overall self-represented appellant population. Self-represented appellants emerging from the Federal Court and Tax Court of Canada are different populations. The former were much more likely to have an abusive litigation history, while the latter voluntarily discontinued appeals, and were never subject to Federal Court of Appeal vexatious litigant management steps. Self-represented appellant proceedings that terminated prematurely or that were conducted by persons who are subject to court access restrictions had significantly more filed documents and docket records. Litigation management steps did not reduce the Registry and Court workload resulting from self-represented appellants subject to court access restrictions. These observations challenge modelling self-represented litigants as a single population with uniform characteristics.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».