Bibliographic record
Abstract
Matters Population Health Jefferson College of Population HealthOn May 30th I traveled to Montreal, Quebec in Canada to participate in a conference unlike any I have ever attended.The three-day Vanguard Conference, sponsored by Next City, is an experiential leadership assembly of 40 leaders whose work is dedicated to improving the quality of life in urban areas.The conference rotates locations each year; the 2017 host was Concordia University and the theme was "accessibility."Next City selected this topic because "21st century urbanism demands that all people enjoy access to the places, tools, and decision-making power necessary to fully participate in urban life and effect change in their community."When I originally applied to become a Vanguard, I was working at Philadelphia Corporation for Aging (PCA), the Area Agency on Aging for the city and county, whose mission is to help older adults remain in their homes and communities for as long as possible.For 8 years I helped our city to become more supportive of people as they age, through influencing policies, plans and programs that increase access to safe and affordable housing, fresh foods, public transportation, and accessible public spaces.Upon transitioning to the Jefferson College of Population Health (JPCH) in January 2017, access was also central to my role, yet in a different way.Access to reliable, safe, and affordable health care that is attuned to the social and environmental determinants of health is a key component of population health.
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.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.030 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.103 | 0.004 |
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".