Bibliographic record
Abstract
In my video, I (James Schlonies) tell the story of how I struggled when I was younger. I went through all mental health services available in my town, but sometimes I just felt worse after. When I was in grade 7, I started going to M’Wikwedong. My family and I received help from M’Wikwedong to cope with what I was going through. Drumming helped me learn to speak up for myself. Now I am a member of the Board of Directors at M’Wikwedong and I am an advocate for other youth in my area. This video was created through a research project entitled: Promoting healthy urban environments for young Indigenous peoples: The case of M'Wikwedong Native Cultural Resource Centre. The research team was formed by the M’Wikwedong Youth Group (Ryerson King, Kaitrina Harrisson, Steven Schlonies, Nikita Jones, and James Schlonies) and the Centre for Environmental Health Equity at Queen’s University (Carlos Sanchez-Pimienta and Jeffrey Masuda). This video displays a previous iteration of the name of this project. M'Wikwedong recently changed its name to "M'Wikwedong Indigenous Friendship Centre."
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.555 | 0.261 |
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".