Bibliographic record
Abstract
This photo is used in the repository. See https://scholarship.law.nd.edu/ndls_student_awards/37/ The following information was provided by: Michael Kennedy (he/him/his) Grants Manager and Program Associate, RRF Foundation for Aging, 8765 West Higgins Road, Suite 430, Chicago, Illinois 60631-4170, Phone 773/714-8080, Fax 773/714-8089 Edward J. Kelly (Ed) was born in Ottawa, Illinois, and graduated from Seneca High School. He attended Notre Dame University where he earned a Bachelor’s Degree [BA 1941] in Political Science and a Doctorate of Jurisprudence [LL.B. 1942]. Before he began his business career, Ed played minor-league baseball. He was also a Navy veteran and honorably served his country during World War II. Ed had a productive career in the insurance industry. He began working at Bankers Life and Casualty in 1959 and rose to become president of three insurance companies owned by John D. MacArthur. He held leadership positions in many other companies. He was a founding member of the Board of Trustees for RRF Foundation for Aging (formerly The Retirement Research Foundation), appointed by the Foundation’s benefactor, Mr. MacArthur. For 45 years, RRF Foundation for Aging has been devoted to improving the quality of life of older people, awarding more than 5,000 grants worth more than $250 million. RRF Foundation for Aging was one of the first private foundations to focus exclusively on aging issues. Ed served as an officer of the Board from 1978 through 2012 and was Chair for 21 years. Upon the termination of their service with our Board, RRF Trustees are able to designate the recipient of an endowment grant. Mr. Kelly chose to award the endowment grant of $75,000 to his alma mater in 2012, a year before his passing. This award established an ongoing speaker series and prize for Law students writing an essay, article, or legal brief on a topic relating to elder law.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.488 | 0.461 |
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".