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Record W2988711850 · doi:10.1093/geroni/igz038.552

UNDERSTANDING INTERGENERATIONALITY: THEORIES, REFLECTIONS, AND EXPERIENCES

2019· article· en· W2988711850 on OpenAlexaffabout
Stephanie Hatzifilalithis, Amanda Grenier

Bibliographic record

VenueInnovation in Aging · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPaceField (mathematics)SociologyOutcome (game theory)PsychologyEpistemologyEconomicsGeography

Abstract

fetched live from OpenAlex

Abstract Research into different aspects of intergenerationalities continues to develop at a considerable pace for individuals, communities, and society more generally. A number of programs and practices for older people are organized around the presumed benefits of intergenerational interaction between younger and older people, with intergenerational programming operating as a taken-for-granted practice. However, the merits of this approach, the models that inform practice, and the learning that takes place between older and younger people, remain under-theorized. This poster reviews and discusses dominant theoretical frameworks including reflections and experiences from intergenerational learning programs in Canada (e.g. Co-Housing). It documents how the field of intergenerationality is conceptualized and executed in the realms of theory and practice; how models retain age and stage-based assumptions, including the polarizing discourses of ‘decline’ and ‘activity’; and discusses the implications for methodology, application, and outcome measures. By understanding the underlying assumptions utilized in the field of intergenerational learning, this poster makes an important contribution to the theoretical foundations, methods, and approaches, that are required to build more appropriate intergenerational landscapes.

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)0.001
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 score0.275
Threshold uncertainty score0.488

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
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 head0.102
GPT teacher head0.365
Teacher spread0.263 · 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

Citations2
Published2019
Admission routes2
Has abstractyes

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