Bibliographic record
Abstract
Canadian child welfare has hit troubled times. The system has been widely and publicly criticized. Its processes have become highly litigious and, in many communities, rigidly managed. For many front line workers, time spent on paperwork outstrips, by far, time spent working directly with families and children. Perhaps as a result, recruitment and retention of staff have become critical problems across the country. At the same time, caseload numbers are climbing steeply, while more and more children are being brought into already burdened alternate care arrangements. When things go wrong, individual parents and workers are blamed, while systemic problems are patched up or glossed over. That child welfare should be so troubled is not surprising. It is a residual, or last resort, service in an increasingly mean-spirited social and economic context. The last decade has seen a substantial retrenchment of the Canadian social safety net, once a source of much national pride. Health care, education and virtually all social services have seen drastic budget cuts in the last few years. Our politicians justify these changes through claims of otherwise insurmountable deficits and loss of competitive edge in the new global markets. Of course, the major victims of this reorganization of wealth and distribution of resources are the poorest and most vulnerable of
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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.031 | 0.008 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.006 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.039 | 0.002 |
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".