Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".