Everyone Swims
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Well-known disparities exist in rates of obesity and drowning, two public health priorities. Addressing these disparities by increasing access to safe swimming and water recreation may yield benefits for both obesity and injury prevention. Everyone Swims, a community partnership, brought community health clinics and water recreation organizations together to improve policies and systems that facilitated learning to swim and access to swimming and water recreation for low-income, diverse communities. Based in King County, Washington, Everyone Swims launched with Centers for Disease Control and Prevention grant funding from 2010 to 2012. This partnership led to multiple improvements in policies and systems: higher numbers of clinics screening for swimming ability, referrals from clinics to pools, more scholarship accessibility, and expansion of special swim programs. In building partnerships between community health/public health and community recreation organizations to develop systems that address user needs in low-income and culturally diverse communities, Everyone Swims represents a promising model of a structured partnership for systems and policy change to promote health and physical activity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it