Transnationalism and identity: a tale of two faces and multiple lives
Bibliographic record
Abstract
Transnational ties add new complexities to the continuous and dynamic processes of identity formation. Based on self‐reflexive narratives, this paper examines the authors’ identity transformations associated with their respective transnational experiences. We are two Asian women from India and China, respectively, who are international students pursuing doctoral degrees in Canada. Although we share similar demographic and economic backgrounds, we perform distinct transnational acts. Focusing specifically on social and cultural linkages, we have identified reasons that have influenced our cross‐border involvements. Based on our findings, we present an emergent conceptual framework that highlights interactive psychological, sociocultural and economic processes that influence the formation of individual transnational identities. We also share with our readers some methodological lessons learnt along our path of self‐expression, analysis and representation .
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.051 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.006 |
| 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".