An Analysis of the Nurturing the Seed Program, Specific to the Treaty 9 Territory Through an Autoethnography LensS
Bibliographic record
Abstract
This research was an opportunity to integrate Indigenous autoethnography within my work while exploring my identity as an Indigenous person, scholar, and researcher. The focus of my work was to critically evaluate the Nurturing the Seed program and its applicability for enhancing child welfare provision services, specifically within the Treaty 9 territory. This was achieved through my participation within the training sessions provided by Infant Early Mental Health Promotion program in collaboration with Sick children (Sickkids, Toronto). Additionally, this thesis also examined peer reviewed literature pertaining to Indigenous child and youth mental health and the importance of early intervention and prevention. Integrating Indigenous autoethnography as a framework within my research permitted space for deep introspection to unfold while allowing me (the subject) to position myself within my work. This self-exploratory journey was grounded on a personal and professional pursuit and interest for aspiring to create change within the child welfare system, in which the integration of my work experiences, life experiences, knowledge, and skillsets acted as guiding principles. Essentially, this journey permitted the opportunity to frame my research within an Indigenist perspective with the use of Indigenous methodologies and principles to ensure the work was conducted in meaningful and respectful approach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".