Bibliographic record
Abstract
Mamow Ki-ken-da-ma-win: Everyone Searching for Answers Together (Searching Together) is a partnership-based approach to identifying priorities and issues of importance in remote First Nations in Ontario. The collective goal of this partnership is to learn from one another in the context of northern First Nations lived realities. This community assessment and mobilization process serves as a vehicle for understanding and taking steps to address community wellness. The Searching Together’s southern team is affiliated with faculty, staff and students at Ryerson University. The voices of youth captured herein are from four fly-in communities in northern Ontario and were gathered between 2011 and 2015. Children and youth played a powerful role in shaping the Mamow Ki-ken-da-ma-win process. The conversations with youth were arranged to accommodate their space and time and were built on established, trusting relationships with the youth facilitators on the team. The youth spoke with candour and had valuable insights into the challenges and opportunities they faced while living in their communities. They were thoughtful about how to overcome obstacles that interfered with their wellbeing and they clearly expressed their wishes, hopes and dreams for the future.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.042 | 0.043 |
| Scholarly communication | 0.021 | 0.022 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.036 | 0.006 |
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".