COLD, HARD JUSTICE LESSONS FROM THE FLEET: INNOVATING FROM THE BOTTOM UP
Bibliographic record
Abstract
With law school graduates encountering increased difficulty in securing articling positions, legal incubators are an alternative way of providing practical training and mentorship opportunities for young practitioners. Not only do they have the potential to help launch careers in law, but they can also play a major role in increasing access to justice. Though legal incubators have been gaining popularity in law schools across the United States, they are still a novel concept in Canada. This article discusses the resources and practice models used by Fleet Street Law, a law practice in Toronto that evolved into the first legal incubator in Canada. The use of innovative business models allowed for greater service of low income and marginalized populations, especially on a “low-bono” rate, and also assisted in providing essential supports for racialized and minority lawyers early in their career. The flexible and innovative nature of a legal incubator was beneficial for the purposes of experimentation, but there were challenges associated with cost and long-term participation. The model of a practitioner-based incubator, as an alternative to traditional-type clinics, should be strongly considered by law schools to help address some of the market needs in the legal community today.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".