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Record W3022039109

Court Fee-waiver Processes in Canada: How Wrong Assumptions, Change Resistance and Data Vacuums Hurt Vulnerable Parties

2020· article· en· W3022039109 on OpenAlexaffabout
Shannon Salter

Bibliographic record

VenueSSRN Electronic Journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsWaiverTribunalBusinessEconomic JusticeLawPolitical scienceLaw and economicsEconomics
DOInot available

Abstract

fetched live from OpenAlex

Court fee waiver processes in Canada, and particularly the process used in British Columbia superior courts, impose extraordinary administrative burdens on low income people by requiring them to navigate needlessly complex, costly, and humiliating procedures to avoid paying relatively modest, yet unaffordable, court fees. There is no empirical evidence supporting this onerous fee waiver process. There is significant empirical and firsthand evidence both that the existing process is harmful to vulnerable parties and that simplifying the process would not lead to an increase in fraud or open the floodgate to applications. The paper sets out key principles in accessible public process design and proposes a framework for human-centred fee waiver processes, based on the Civil Resolution Tribunal’s process which was co-designed with community legal advocates. In closing, the paper argues that court fee waiver processes are just one example of our public justice system’s persistent failure to challenge unfounded assumptions with empirical evidence, and design based on public need. In doing so, the justice system disproportionately hurts already marginalized people, compromising the rule of law and the constitutionally protected right to access the courts.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.900
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.002
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 head0.104
GPT teacher head0.342
Teacher spread0.238 · 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 designNot applicable
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

Citations2
Published2020
Admission routes2
Has abstractyes

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