Californians Linking Action with Science for Prevention of Breast Cancer (CLASP-BC)—Phase 2
Bibliographic record
Abstract
Californians Linking Action with Science for Prevention of Breast Cancer (CLASP-BC) is part of California Breast Cancer Research Program's (CBCRP) Initiative strategic priority to disseminate and implement high-impact, population-based primary prevention interventions. CLASP-BC is informed by six years of funded program dissemination and implementation (D&I) research and evaluation conducted by the Canadian Partnership Against Cancer (CPAC) through its Coalitions Linking Action and Science for Prevention (CLASP). In its second phase, CLASP-BC will fund multi-sector, multi-jurisdictional initiatives that integrate the lessons learned from science with the lessons learned from practice and policy to reduce the risk of developing breast cancer and develop viable and sustainable infrastructure models for primary prevention breast cancer programs and research evidence implementation. Applications will be solicited from research, practice, policy, and community teams to address one or more of the intervention goals for the 23 risk factors identified in Paths to Prevention: The California Breast Cancer Primary Prevention Plan (P2P), expanding upon existing primary prevention efforts into two or more California jurisdictions, focused on disadvantaged, high risk communities with unmet social needs. The lessons learned from CLASP-BC will be widely disseminated within the participating jurisdictions, across California and, where applicable, to jurisdictions outside the state.
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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 | 0.048 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".