Haunted borders: Temporary migration and the recalibration of racialised belonging in Australia
Bibliographic record
Abstract
The shift away from a focus on permanent settlement and towards a temporary migration paradigm is remaking Australia’s borders in the twenty-first century. Through an empirical case study of Indian migrants in Australia this thesis examines the implications of the shift to temporary migration for the racial belonging of migrants in Australia. Adopting an intergenerational approach it examines data from interviews conducted in Sydney with Indian migrants who are long-term settlers in Australia, as well as those who migrated from India from the year 2000 on temporary visas. Interviews with key informants, government officials, and local service providers are also analysed to provide insights into the governmentality of temporary migration. The relative paucity of theorising on race in studies of contemporary migration gives race a spectral quality that is ever-present but barely articulated. This thesis seeks to render speakable the ghost of race in migration. Firstly, I analyse the nation’s border as a space haunted by the ghost of race. I argue that historicising the use of temporary migration in transnational settler colonial contexts, including through a comparative study of temporary migration in Canada, is crucial to illuminating the contemporary function of temporary migration as a racial biopolitical technology of the border. Secondly, I analyse three key ways in which temporary migration functions as a racial biopolitical technology. The biopolitical modality of chronopolitics produces temporary migrants as precarious subjects by conditioning their relation to the present and future. The system of governmentality through which temporary migration is administered further conditions the precarity of temporary migrants, while ensuring their externalisation from national social policy frameworks for migrant integration. The Australian state then regulates the link between temporary and permanent migration using the body of the temporary migrant to draw the racial fault line between the ‘desirable’ settler subject and ‘undesirable’ disposable migrant subject. Finally, I examine how Indian migrants negotiate haunted borderscapes to make their own futures through temporalities of hope that exceed nationalist constructs of belonging.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".