VOICES FROM THE FIELD- A Prevention Project For Low-Income Families
Bibliographic record
Abstract
“Frontline workers often use current empirical research as guidelines to make decisions when developing new programs or refining existing ones, ” says Leslie McDiarmid, Project Coordinator of Better Beginnings, Better Futures in Ottawa, Ontario. Better Beginnings, Better Futures is a community-based primary prevention research initiative for young children (from birth to age five) and their families living in disadvantaged communities in Ontario. McDiarmid works with young children and their parents in specific Ottawa neighbourhoods where children are at risk for developmental problems. The CEECD papers on low income relate to some situations that McDiarmid sees in her work.1-6 What are the implications of the research findings in the CEECD papers for your work? Most research is done in a specific context. Better Beginnings, Better Futures uses research information and applies research findings that are relevant to their settings. For instance, about a year ago, Better Beginnings, Better Futures introduced Books for Babes, a new program for children in their community based on research indicating that young
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".