Bibliographic record
Abstract
Deborah is a music therapist and pianist working in the Waterloo Region. She began her music studies at the University of Toronto, and completed her Honours Music Therapy with General Psychology degree at Wilfrid Laurier University in 2004, where she received the President’s Scholarship within the Faculty of Music. She interned in the long-term and palliative care units at Sunnybrook Health Sciences Centre and received Music Therapist Accredited (MTA) status with the Canadian Association for Music Therapy in 2005. After working with children and adolescents with special needs in the 2 Durham Region, Deborah returned to Wilfrid Laurier University and completed the Master of Music Therapy (MMT) program in 2007. During her graduate studies, Deborah explored some of her many interests in music therapy, including: clinical improvisation, consciousness, and working with people experiencing mental health issues. She also sat on the Laurier Centre for Music Therapy Research board. Deborah’s other creative endeavours include playing piano and keyboard with the Kitchener-Waterloo Improviser’s Collective and singing with the Woodstock-Fanshawe Singers. Deborah’s music therapy practice, Inspira Music Therapy, is currently based in Waterloo, Ontario.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.119 | 0.064 |
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".