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Record W2901320521

Aging Well: User Centred Principles for Aging in Community

2018· other· en· W2901320521 on OpenAlexaboutno aff
Jennifer Recknagel

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2018
Typeother
Languageen
FieldSocial Sciences
TopicCollaborative and Sustainable Housing Initiatives
Canadian institutionsnot available
Fundersnot available
KeywordsGrassrootsAging in placePublic relationsRetirement communityDocumentationGerontologyIndependent livingExploratory researchSociologyPsychologyPolitical scienceMedicineComputer scienceSocial science
DOInot available

Abstract

fetched live from OpenAlex

Aging Well is an exploratory research project aimed at uncovering grassroots models of seniors’ supportive living that are emerging across Canada and the United States. The research takes a lead user approach to uncover and describe new models of senior living and supportive care that are developing to address the issues of aging in place and seniors social isolation. \n \nEthnographic case studies of four grassroots initiatives are discussed, including Homesharing, Senior Cohousing, Naturally Occurring Retirement Communities with Social Service Program (NORC-SSP), and Virtual Villages. Each case study includes audio documentaries of user experiences and photographic documentation of community life. A distillation of key elemental practices by user innovators, along with corresponding design principles for aging well in community is offered. This project is intended to inform the future development of seniors’ supportive living initiatives.

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)0.023
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.023
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0230.019
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0040.014
Scholarly communication0.0110.011
Open science0.0040.010
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0080.004

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.

Opus teacher head0.137
GPT teacher head0.375
Teacher spread0.237 · 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 designQualitative
Domainnot available
GenreEmpirical

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
Published2018
Admission routes1
Has abstractyes

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