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Record W2116979554

Age-Friendly Communities Principles and Initiatives

2014· article· en· W2116979554 on OpenAlexaffvenueabout
Nancy E. Newall, Verena Menec

Bibliographic record

VenueCanadian acoustics · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsPremiseUrbanizationEconomic growthPolitical scienceGeographyPublic relations
DOInot available

Abstract

fetched live from OpenAlex

In Canada, as in many countries around the world, the proportion of older adults is increasing. In addition, more people of all ages are living in cities. The Cities concept was spearheaded by the World Health Organization (WHO) in response to these two major world-wide trends of global aging and urbanization. The underlying premise of age-friendly cities is that they foster the health, security, and social participation of older adults, or what the WHO refers to as 'active aging.' In 2007, the WHO launched the Global Cities: A guide which identified core age-friendly city features. As a follow-up, Canada launched the Age-Friendly Rural and Remote Communities: A guide which provided a framework for understanding some of the issues faced bv the smaller communities characteristic of Canada and other parts of the world. Along with several other provinces within Canada, Manitoba has embraced the concept of creating age-friendly communities and is recognized as a global leader in the initiative. This presentation will provide a background on the Cities initiative and on creating age-friendly environments. Particular attention will be paid to the process of creating age-friendly communities in Manitoba and possible ways that the physical and social environment can intersect with issues of hearing, communication, and social participation of older adults.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: Empirical
Teacher disagreement score0.699
Threshold uncertainty score0.880

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.267
Teacher spread0.239 · 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 teacher head, 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

Citations1
Published2014
Admission routes3
Has abstractyes

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