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Record W2726294924 · doi:10.1093/geroni/igx004.3024

BUILDING CONNECTIONS BETWEEN ELDER LAW AND GERONTOLOGY

2017· article· en· W2726294924 on OpenAlexaboutno aff
Nina A. Kohn, Israel Doron, María Brown

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicElder Abuse and Neglect
Canadian institutionsnot available
Fundersnot available
KeywordsScholarshipField (mathematics)SpecialtySociologyValue (mathematics)LawGerontologyPolitical sciencePsychologyMedicineFamily medicine

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.031
Threshold uncertainty score0.163

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.043
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.003
Science and technology studies0.0160.036
Scholarly communication0.0120.019
Open science0.0020.031
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.086
GPT teacher head0.402
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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