Bibliographic record
Abstract
1. Introduction, by Brian Wharf 2. Getting to Now: Children in Distress in Canada's Past, by Veronica Strong-Boag 3. Community Social Work in Two Provinces I. The Neighbourhood House Project in Victoria and the Hazelton Office of the Ministry for Children and Families, Brian Wharf II. Community Child Welfare: Examples from Quebec, by Linda Davies, Karen Fox, Julia Krane, and Eric Shragge 4. Community Organizing in Child Welfare I. Changing Local Environments and Developing Community Capacity, by Brad McKenzie II. Child Protection Through Strengthening Communities: The Toronto Children's Aid Society, by Bill Lee and Sharon Richards III. Learning from the Past / Visions for the Future: The Black Community and Child Welfare in Nova Scotia, by Candace Bernard and Wanda Thomas Bernard 5. Community Control of Child Welfare: Two Case Studies of Child Welfare in First Nations Communities I. Watching Over Our Families and Children: Lalum'util' Smun'eem Child and Family Services, by Leslie Brown, Lise Haddock, and Margaret Kovach II. Building Community in West Region Child and Family Services, by Brad McKenzie 6. Searching for Common Ground: Family Resource Programs and Child Welfare, by Janice McAulay 7. Building a Case for Community Approaches to Child Welfare, Brian Wharf Contributors Index
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.008 |
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".