Identity (Geo)Politics: Pakistani Communities and the Nation State System
Bibliographic record
Abstract
My dissertation explores the relationships between states in the age of globalization, and the construction and expression of Pakistani immigrants’ national, ethno-racial, and religious identities at the individual and group levels. It is driven by the question of how we make sense of boundaries and belonging, and I explore the relationship(s) between immigrant communities’ identities and local, state, and global contexts. Concretely, I examine how Pakistani immigrants in the global metropolitan areas of San Francisco, California and Toronto, Ontario construct and participate in various identity communities both within and across borders. I argue that accurate study of global phenomena requires we question the assumptions built into the nation-state system, which overly constrain our analyses and representations of reality. I demonstrate the import of American hegemony, and show how the US and Canada, in their separate and dialectic ways, create visually-identified others. I suggest an analytic framework of the administrative and the affective of the macro and the micro, and apply it to citizenship to comment upon the processes of racial disciplining that structure subjectivities in an increasingly interconnected global society. This dissertation adds to the little discussed question of how race is made across boundaries, shows how states structure subjectivities, and how immigrants’ identity projects are instances of the deterritorialization of the nation-state.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.020 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".