Traditional Indigenous Approaches to Healing and the modern welfare of Traditional Knowledge, Spirituality and Lands: A critical reflection on practices and policies taken from the Canadian Indigenous Example
Bibliographic record
Abstract
Traditional Indigenous Approaches to Healing and the modern welfare of Traditional Knowledge, Spirituality and Lands: A critical reflection on practices and policies taken from the Canadian Indigenous Example In order for traditional knowledge to be maintained and to develop, it has to be practiced. Traditional healing provides a vehicle for this to occur. In Canada, the spiritual revitalization of Indigenous communities and individuals often involves the use numerous components of traditional healing. These elements are reflected most clearly at the grassroots level, however, current Indigenous programs delivered by Indigenous and governmental agencies have made some accommodating efforts as well. Perhaps most importantly, traditional knowledge and Indigenous spirituality hinges on the maintenance and renewal of relationships to the land. Indigenous land bases and the environment as a whole remain vitally important to the practice of traditional healing. A focus on Indigenous healing, when discussing Indigenous knowledge systems and spirituality, is paramount today due to the large scale suppression of Indigenous cultural expressions during the process of colonization. With respect to policy, there appears to be a historical progression of perception or attitude towards Indigenous traditional healing in Canada from one of disfavour to one favour. There are nevertheless continuing challenges for traditional healing. Mainstream perceptions and subsequent policy implementations sometimes still reflect attitudes that were formulated during the decline of traditional healing practice during colonization processes. As a consequence the ability for particular communities to maintain and use their specific understandings of Indigenous knowledge continues encounter obstacles. Indigenous Knowledge systems are living entities and not relics of the past. Today, these knowledge systems are still greatly being applied to help Indigenous communities and Indigenous people recover from intergenerational pain and suffering endured during the colonization process. Future policy development and implementation should aim to support Indigenous peoples and communities when they decide to learn about, maintain and build upon the knowledge amassed by their ancestors.
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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 | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.064 | 0.047 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.002 | 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".