Bibliographic record
Abstract
Myrna Lynne McCallum is a bi/2S Métis grandmother, podcaster, global educator, lawyer and inter-generational healer from Green Lake, Saskatchewan in Treaty 6 territory who currently resides on the ancestral, unceded and traditional territories of the Squamish, Tsleil-Waututh and Musqueam people. In this chapter she shares some of her journey to becoming a trauma-informed lawyer and how she came to understand that trauma is a traveler who follows into relationships and workplaces. In offering her insights, Myrna is attempting to help lawyers and judges begin to see trauma in all its forms so they can make space for it to move through as quickly as possible while taking steps to ensure that they do not get stuck in its grasp. By offering her insights, Myrna hopes that the lessons that she learned along the way can also educate court clerks, correctional officers, child protection workers and police officers working in various legal or justice systems. By promoting a trauma-informed practice, Myrna is inviting hope, humanity and healing into the workplace.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.012 |
| Insufficient payload (model declined to judge) | 0.039 | 0.018 |
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".