Horse as Healer: An Examination of Equine Assisted Learning in the Healing of First Nations Youth from Solvent
Bibliographic record
Abstract
1. Contributions: This article is a collaborative community-based effort of the identified authors, with Ernie Sauve representing Elders Herb, Gladys, and John (a pseudonym), staff and members of the Board of Directors of the White Buffalo Youth Inhalant Treatment Centre, and Tamara MacKinnon representing the staff of the Cartier Equine Learning Centre. Acknowledgement: Two of the authors of this article, Darlene and Colleen, were driving from Saskatoon to Sturgeon Lake First Nation to meet with two of the Elders who are trusted with providing spiritual and cultural guidance at the White Buffalo Youth Inhalant Treatment Centre. As they passed a roadside cross (referred to by Darlene as a shrine) to mark the passing of an individual(s) through a motor vehicle accident, they began a discussion that observed how these “shrines” would not exist to the extent they do if there were no vehicles, and, in place, horses were once again the primary mode of transportation. When their morning meeting with the Elders was coming to a close, a final thought was shared by one of the Elders, Herb. He said that the iron horse (i.e., automobile) has contributed its share to the illness (e.g., materialism) that plagues society today. Another of the Elders, Gladys, reflected that all is interconnected in life, and everything (e.g., seasons) comes full circle. In many ways, this article represents a piece of the circle of life for each of the authors. We are happy to be able to share this with the reader, and are grateful for what we have learned in the process.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.013 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".