D.B.S. v. S.R.G.: Promoting Women's Equality through the Automatic Recalculation of Child Support
Bibliographic record
Abstract
This article presents the arguments of the Women's Legal Education and Action Fund (LEAF) on the issue of retroactive child support. It argues that the systemic inequality experienced by women both prior to and after separation must not be exacerbated by an interpretation of the legislative child support regime that fails to recognize the feminization of poverty associated with the caregiving of children. By considering the family law case of D.B.S. v. S.R.G. and the legislative history of the child support regime, the authors contend that the Alberta Court of Appeal's progressive approach regarding the automatic calculation of child support best supports women's equality. The Court of Appeal's approach in D.B.S. is grounded in the principles of statutory interpretation, it is in accordance with the equality guarantee of the Canadian Charter of Rights and Freedoms, and it complies with international human rights obligations. The D.B.S. approach is contrasted with another line of reasoning in the jurisprudence that is more restrictive in its recalculation of child support and, accordingly, contributes to women's inequality. The article concludes by endorsing the D.B.S. approach, acknowledging the perils of reprivatization and critiquing the Supreme Court of Canada's recent decision in this case.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.015 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".