Family Diversities and Wellbeing Framework
Bibliographic record
Abstract
Why family diversities? Many of our contemporary conversations about families in Canada are about how they have changed in ways that make them more diverse. There is much to celebrate in these diversities and in the family mosaic that they create. Yet we also see that while some families are thriving, others are marginalized. To date, we have not had a way of systematically thinking about family diversities or about the inequalities that may be inherent in them. Across the variety of families in Canada, it is important to map what is known, where knowledge gaps exist and where we need to create evidence that can inform policies, programs and services to better support family wellbeing. A roadmap for understanding family diversity Developed by the Vanier Institute of the Family1 through consultation with academic and government partners and grounded in family research, the Family Diversities and Wellbeing Framework views family diversity through three intersecting lenses: Family Structure, Family Work, and Family Identity. Each lens focuses on a different way of seeing families. Each illuminates factors that can either enhance or detract from family wellbeing. Each provides a way of highlighting where our understanding is currently limited. Below, we outline what each lens means, why it matters, and priority areas for consideration based on current issues in Canadian society.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.017 | 0.024 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".