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Record W2107616442 · doi:10.1017/s0144686x11000511

Theorising about ageing, family and immigration

2011· article· en· W2107616442 on OpenAlexaff
Lynn McDonald

Bibliographic record

VenueAgeing and Society · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsInstitute of AgingUniversity of Toronto
Fundersnot available
KeywordsPerspective (graphical)ImmigrationSociologyEthnic groupPositivismPostmodernismVariety (cybernetics)EpistemologyPolitical scienceComputer scienceLaw

Abstract

fetched live from OpenAlex

ABSTRACT This article critically reviews the ways in which theories in social gerontology have been used to explain the influence of ethnicity and immigration on older adults. The purpose of this cursory examination is to explore how these theories can be used for further theory advancement without serious modifications to existing theories. Current theories do not have the capacity to capture the effects of immigration at the structural level and its link to the social, psychological and family levels, let alone the physical ageing of individuals or societies. The overriding complexity of ageing and immigration requires at minimum, a long-term view and an integrating framework with multiple levels that can accommodate a variety of theoretical interests. The conclusions are that a lifecourse perspective, in conjunction with existing theories, can be employed in two different ways to further this theoretical agenda. A lifecourse perspective provides scaffolding for other theories where seemingly incommensurate epistemological positions can be easily accommodated and the principles of the lifecourse can be integrated into existing theories for a more fine-grained analysis of ethnicity and immigration. These approaches leave the theoretical door open to everyone including the positivists, the constructionists and critical and postmodern scholars with the possibility for interdisciplinary theory building to advance the understanding of the lives of immigrant families.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.553
Threshold uncertainty score0.668

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 head0.016
GPT teacher head0.257
Teacher spread0.241 · 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 designTheoretical or conceptual
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

Citations47
Published2011
Admission routes1
Has abstractyes

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