Aligning research to meet policy objectives for migrant families: an example from Canada
Bibliographic record
Abstract
BACKGROUND: 'Evidence-based policy making' for immigrants is a complicated undertaking. In striving toward this goal, federal Canadian partners created the Metropolis Project in 1995 to optimize a two-way transfer of knowledge (researchers - policy makers) within five Canadian Centres of Excellence focused on migrants newly arrived in Canada. Most recently, Metropolis federal partners, including the Public Health Agency of Canada, defined one of six research priority areas as, immigrant 'families, children, and youth'. In order to build on previous work in the partnership, we sought to determine what has been studied within this research-policy partnership about immigrant 'families, children, and youth' since its inception. METHODS: Annual reports and working papers produced in the five Centres of Excellence between 1996-2006 were culled. Data on academic works were extracted, results coded according to eleven stated federal policy priority themes, and analyzed descriptively. RESULTS: 139 academic works were reviewed. All federal priority themes, but few specific policy questions were addressed. The greatest volume of policy relevant works were identified for Services (n = 42) and Education and Cultural Identity (n = 39) priority themes. CONCLUSION: Research conducted within the last 10 years is available to inform certain, not all, federal policy questions. Greater specificity in federal priorities can be expected to more clearly direct future research within this policy-research partnership.
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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 | 0.037 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.013 |
| Science and technology studies | 0.041 | 0.008 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.003 |
| 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".