Bibliographic record
Abstract
Many in the justice system know that fundamental change is needed but few know the best way to do it. Previous attempts using strategic planning approaches have not achieved meaningful change. Something different is needed. The BC Family Justice Innovation Lab (the Lab) is experimenting with a different approach drawing on complexity science, the experience of other jurisdictions and disciplines and incorporating human-centred design as a way of focusing on the well-being of families going through the transition of separation and divorce. This article is the story of the first few years of the Lab’s life. It has been a fascinating and challenging path so far, and it remains to be seen whether it will ultimately succeed. The story is offered so that others with similar ambitions can learn from the Lab’s experience – its successes and its failures. It is the nature and strength of stories that the reader will take from them what they will. For the authors, one overriding theme that emerges from this story is that transforming a complex social system, such as the family justice system in British Columbia, requires embracing the complexity of paradox and refusing to be defeated by the tension of opposites and a multitude of wicked, unanswerable questions.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.018 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.016 | 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".