Population Change and Lifecourse: Strategic Knowledge Cluster
Bibliographic record
Abstract
Population issues provide an important part of the context within which significant social policy choices are made. Canada is experiencing low fertility, profound changes in family life, slow population growth largely driven by immigration, population aging, and important regional differences in the pace of both population growth and population aging. While the significance of demography is widely acknowledged, understanding of the determinants and consequences of demographic change for individuals and communities is limited. The objective of this proposal is to bring the fruits of demographic and social research to the forefront of discussion of social policy by bringing a demographic perspective into contact with a lifecourse perspective on individual and social change. The lifecourse perspective allows researchers, policymakers, and other stakeholders to see how broad changes in the make-up of our population affect the key decisions that individuals make and the transitions they experience in life. This focus on transitions promises to enrich social policy debate and open the door to innovations that will more effectively address new social challenges faced by individuals and communities. To accomplish this, we propose to bring together two accomplished research clusters that had previously received SSHRC support. The Population Change and Public Policy cluster will join with a group of
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".