Bibliographic record
Abstract
From the mid 1950s, music therapy in Canada had grown steadily and surely. As federal and provincial funding began to shrink and fiscal restraints hit, it was time for us to take action to ensure that funds would be available for the further development of music therapy in this country.And so the Trust Fund was formed in 1994 through the efforts of Bernadette Kutarna, Doug Ramsay, Susan Summers, and Colleen Purdon. It took about two years to get set up and organized, which was accomplished under the stewardship of Colleen Purdon. Fran Herman took over as Chair in May of 1995. At that time the Board felt it was ready to start fundraising.It was apparent that we needed to seek possible donors. We knew that the rock industry in England was helping to support music therapy there and so decided on a similar approach for our own fund.In August of 1995, John Marshall, then producer of the New Music at MuchMusic, agreed to join the Trust Fund board at the invitation of Fran and Carl Herman. Marshall had previously contacted Fran Herman while working on a television story about Music Therapy. He told his boss Denise Donlon what he wanted to do, she gave her blessing, and that first step changed the history of music therapy in Canada forever. Within ten years the Trust Fund has raised $4 million, given assistance to more than 340 projects from coast to coast and into the Yukon, and has raised the profile of music therapy in this country.It took about a year to involve the different labels that were operating in Canada at that time. These included, Polygram, ASM, Sony, Warner, Universal, MCA Concerts, BMG, Virgin, EMI, and Zomba. As well, HMV, MuchMusic, CHUM and other industry initiatives also brought support. By June 1996, BLUESBERRY JAM, the first benefit for the Trust Fund, was held in Toronto, and the 10th Anniversary THE BEAT GOES ON, was celebrated in May 2006.Successful fundraising requires imagination, dedication, creativity and most of all, a great sense of fun. Over the years this nonprofit organization has been blessed with an abundance of each. To date, some examples of our fund-raisers include:* A nostrologer who told your fortune by reading noses* Motorcycle rides up Whistler Mountain* Marathon runs* Weaselpaloozas and Schmoozes* A perfectly normal, well-balanced man with hair down to his waist who charged everyone to cut it off* Celebrities auctioned off as slaves for a day* SWAG auctions on e-Bay of Ricky Martin's pants, Elton John's piano stool, Mick Jagger's harmonica and guitars signed by legends like Jeff Beck, The Guess Who, Joni Mitchell, Lenny KravitzIn addition, rock bands, teen buskers, big name producers and well-known companies have all climbed aboard this crazy ride. It's been awesome! It's been great! It has made a difference to thousands of people all across this country.Once funds began to roll in, the Trust drew up a blueprint as to how distribution would take place. Twice a year, therapists are able to submit proposals that are given to a group of five people to judge and prioritize. This committee is made up of four accredited therapists from CAMT, as well as one outside professional. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".