Literature review on transnational grandparent migration: A view from Australia
Bibliographic record
Abstract
Given the phenomenal increase in transnational mobility, grandparents, like many others, are engaged in and influenced by this trend. Often grandparents are “left behind” when their adult children move to distant places (Ariadi, Saud, & Ashfaq, 2019; Evandrou, Falkingham, Qin, & Vlachantoni, 2017; Falkingham, Qin, Vlachantoni, & Evandrou, 2017; Deependra Kaji Thapa, Visentin, Kornhaber, & Cleary, 2018; Deependra K. Thapa, Visentin, Kornhaber, & Cleary, 2020; Zickgraf, 2017). Others become “flying grannies” or “older migrants” who either temporarily or permanently migrate to where their children are to provide or receive care and support (Hamilton, Kintominas, & Adamson, 2021; King, Cela, Fokkema, & Vullnetari, 2014; Plaza, 2000; Ran & Liu, 2021; Subramaniam, 2019; Wyss & Nedelcu, 2018). Over the past two decades, researchers have become increasingly interested in transnational migrant grandparents and their roles in childcare and home care for transnational families (Askola, 2016a; Ho & Chiu, 2020; King et al., 2014; Lamas-Abraira, 2019; Plaza, 2000; Ran & Liu, 2021; Shih, 2012; Solari, 2017; Tezcan, 2021; Treas, 2008; Treas & Mazumdar, 2004; Wyss & Nedelcu, 2018). We gathered background information for a project on families’ roles in migration. We are specifically interested in representing perspectives from the Global South and North so the project is called the Decentering Migration Knowledge (DEMIKNOW) and includes scholars associated with migration research centres from Australia, Canada, China, and India. This literature review synthesizes current scholarship and identifies what research is needed from an Australian perspective.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".