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
Sean Maher joined the Department of Surgery in March 2021 as Senior Business Manager, bringing a decade of experience in healthcare management. As an undergraduate at the Pennsylvania State University, he earned a degree in Health Policy Administration. After graduation, he spent more than five years at the University of Pennsylvania Health System, starting in the Department of Neurology. After moving to the Temple Health System, he discovered that his true passion is in the financial – not operational – side of healthcare management. While at Temple, he earned an MBA from Gwynedd Mercy University and advanced to become Senior Financial Analyst for projections, handling budgets for the entire health system. As the Department of Surgery’s Senior Business Manager, Maher is applying his skills in financial management and analysis. In addition to having overall responsibility for the Department’s budget, Maher is working to streamline the collection, analysis and dissemination of financial data to clinicians within the Department. He is building a financial model to help physicians to better understand and continually improve their own performance data. “I’m most excited about getting this statistical information to the physicians on a monthly basis,” he says. “Once everything is automated, they’ll be able to drill down to see the financial information associated with the procedures they are performing.” Maher, who grew up in Northeast Philadelphia, met his wife in Sea Isle City (his favorite vacation). They now live in Upper Dublin with their daughter, Quinn, who turns two in August.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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