Bibliographic record
Abstract
Transnationalism has been explored in scholarship within the scope of migration and globalisation. Often, related literature and theories conceptualise and deconstruct transnationalism within the framework of methodological nationalism. However, new scholarship suggests that the outdatedness of this framework results in its failure to adapt to, capture, and better understand the complexity and intersectionality of transnationalism in the contemporary context. Drawing on an autoethnographic account of my transnational relationship with my grandmother, this paper delves into the individual everyday perspective of transnationalism and the impacts it has had on facilitating the emergence of transmigrant identities, creating a sense of belonging, and transforming care networks that span the globe. It is a collection of short stories that o ers a level of insight, meaning, understanding, being there, and co-presence (Baldassar, Nedelcu, Merla, and Wilding 2016) that exempli es the contemporary transnational relationship and the ways in which it has been shaped by the digital space, rapid globalisation, and narratives of migration. By researching transnationalism from a qualitative and ethnographic methodology, this paper explores the embeddedness of a “here and there” dynamic (Duong 2015, 233) that has come to re ect a profoundly felt sense of interconnectivity between loved ones. Finally, this study prompts existing researchers and scholarship to critically engage with and adapt their ways of researching transnationalism in the global context.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".