Better Beginnings, Better Futures: Theory, research, and knowledge transfer of a community-based initiative for children and families
Bibliographic record
Abstract
Better Beginnings, Better Futures is an early childhood initiative focused on promoting healthy development of children and families in economically disadvantaged communities. The Better Beginnings approach is ecological and holistic, community-driven, integrated with existing community services and supports, and universally available to children aged 4-8 within communities in which it is offered. The Better Beginnings initiative effectively illustrates the concept of wellness as fairness through its efforts to create more just social conditions and its connection to both procedural and distributive justice, the two principles off airness outlined by Prilleltensky (2012). Through the development of programs that support children, parents, families, and the community as a whole, Better Beginnings initiatives are able to promote children's development by building community capacity to create healthy and positive environments for children. This paper provides an overview of the Better Beginnings, Better Futures initiative from its outset in 1990 to the present, with a view towards examining the ways in which knowledge generated from such initiatives can be transferred to other communities.
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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 | 0.016 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".