Bibliographic record
Abstract
Indigenous and non-Indigenous scholars forward child welfare issues currently impacting Indigenous children in Canada. Walking Together is the seventh title in the Voices of the Prairies series. Developed by the Prairie Child Welfare Consortium, this edited collection brings together accomplished Indigenous and non-Indigenous scholars from the prairie provinces to forward critical research about a range of contemporary child welfare issues currently impacting Indigenous children in Canada. Centering Indigenous knowledge and working to decolonize child welfare, contributors address the over-representation of Indigenous children in the child welfare system, the un-met recommendations of the TRC, the connections between colonialism and fetal alcohol spectrum disorders, the impact of Bill C-92, and more. Contributors include: Jason Albert, Dorothy Badry, Cindy Blackstock, Elder Mae Louise Campbell, Peter Choate, Linda Dano-Chartrand, Michael Doyle, Koren Lightning Earle, Arlene Eaton Erickson, Yahya El-Lahib, Hadley Friedland, Don Fuchs, Del Graff, Jennifer Hedges, Bernadette Iahtail, Jennifer King, Brittany Mathews, Eveline Milliken, Kelly Provost—Ekkinnasoyii (Sparks in a Fire), Christina Tortorelli, Gabrielle Lindstrom Tsapinaki, Susannah Walker, and Robyn Williams
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.003 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.267 | 0.097 |
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".