Family Legal Services Review Submission on Unbundling & Legal Coaching
Bibliographic record
Abstract
Legal coaching is a form of unbundling that, as Justice Bonkalo notes, “is uniquely characterized by the lawyer equipping the client to move his or her own matter forward (by reviewing documents, preparing them for an appearance, etc.) rather than personally doing the work for the client.”\nWhile the practice of legal coaching is not new – lawyers have been doing this informally for years – the term “coaching” was coined by Dr. Julie Macfarlane in her groundbreaking 2013 National Study on SRLs, which included interviews or focus groups with 259 SRLs from Alberta, British Columbia, and Ontario, as well as with 107 court staff and service providers. A great deal of attention has been paid to Dr. Macfarlane’s findings regarding motivation, challenges and impact. I will not review these findings in detail, but I observe in summary that the number one reason respondents provided for being self-represented was cost, the main challenge they reported was dealing with the complexity of the system, and the biggest impact they experienced was the extreme stress and anxiety of navigating this complex system on their own.1
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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.188 | 0.088 |
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".