Bibliographic record
Abstract
It is my great pleasure to present the very first audiovisual edition of the First Peoples Child & Family Review! This is a peer reviewed journal dedicated to interdisciplinary knowledge honouring the voices and perspectives of First Nations, Métis, Inuit and other Indigenous people around the world. With this edition we want to highlight the importance of oral and visual knowledge transmission for Indigenous people. We want to challenge the prevalence of written knowledge transmission in academia and show that other ways of knowing are just as powerful. This includes publishing work from people young and old in recognition that learning is a lifelong journey and that each and every one of us carries wisdom in our hearts and in our minds. In total, there are 12 works published in this edition of the First Peoples Child & Family Review. This includes drawings and videos from Indigenous researchers, graduate students, community members, youth, and non-Indigenous allies and supporters. I want to thank all of the contributors to this special edition for sharing their knowledge, experience, and stories and to the peer referees for their thoughtful feedback. To you, the viewer, I hope you enjoy learning from and being inspired by the contributions that follow.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.425 | 0.342 |
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".