BUILDING CONNECTIONS BETWEEN ELDER LAW AND GERONTOLOGY
Bibliographic record
Abstract
Over the past several decades, elder law has emerged as an important legal specialty. Lawyers, clients, and, law schools are increasingly recognizing the value of the field and its practical and intellectual rigor. Despite this development, however, elder law remains at the periphery of the study of aging and has yet to be meaningfully integrated into the larger field of gerontology. This lack of integration is unfortunate for two primary reasons. First, elder law practice would benefit from being informed by the larger study of aging. Second, an understanding of elder law is integral to understanding of the experience of growing older. To investigate the current relationship between elder law and gerontology and opportunities for (and barriers to) connecting the fields, we conducted structured interviews with 27 leading professors of gerontology and elder law in the United States, Canada, and the United Kingdom. Interviews were designed to: 1) identify existing attitudes toward elder law among those working in the field of gerontology, and existing attitudes toward gerontology among those working in the field of elder law; 2) identify opportunities for, and barriers to, connecting teaching and scholarship in the two fields. In this session, we will present our findings related to existing and potential connections between the fields of gerontology and elder law in scholarship and participant suggestions for improving the connection between the fields. Based on these findings, we will then help participants identify steps they might take to connect and integrate the two fields in their scholarship and teaching.
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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 | 0.031 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.016 | 0.036 |
| Scholarly communication | 0.012 | 0.019 |
| Open science | 0.002 | 0.031 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".