In-betweeness: the (dis)connection between here and there. The case of Indian student-migrants in Australia
Bibliographic record
Abstract
In recent years the number of Indian international students in Australia has increased considerably, from less than four hundred in the early 1990s to close to a hundred thousand by the end of 2009. This phenomenal growth is, to a large extent, due to the fact that a majority of Indian student intends to apply for permanent residency after graduation for which the Australian state had designed clear pathways. As a result education and migration have become highly entangled in Australia. This paper will analyze what it means for young, middle class Indians, to be both students and migrants at the same time. I will do so using the concept of in-betweenness – falling in-between commonly recognized categories – often understood as an ‘accidental state of being’ in literature on migration and transnationalism. I will show, however, that Indian student-migrants very actively seek out this particular state of being as an end goal by itself. As a result this paper will be able to shed light on what – in the Indian case - the (de)coupling of the local and global means, both theoretically and in practice. I will finally make a case against hegemonic ideas of integration which still lean heavily on neoliberal push-and-pull migration models and argue that in order to understand current day migration we need to be open to the possibility that many migrants do not so much seek to integrate themselves in the local but much more into the global.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".