Bibliographic record
Abstract
Immigration is a central site through which national communities are institutionally imagined and materially constructed. The borders of these imagined communities are generated in part through state policies, particularly immigration policies. Using Canada as a point of departure, this article will question how the cultural politics of immigration are shaped through media and policy discourses of immigration. In settler nations such as Canada, the long tradition of media spectacles around immigration is a key site for the amplification of political affect around national belonging that strongly impinges upon immigration policy formation. Drawing on the Foucauldian governmentality literature and its focus on the population as an object of governance, two particular articulations of immigration as a means of regulating the population are considered: first, the articulation of immigration with questions of fertility and sexuality; and, second, the dramatically heightened media and policy articulations of immigration with security. This article questions how we might begin to account for the political affect elicited in media culture around `desirable' and `undesirable' immigrants/refugees and its impact on the regulation and governance of immigration.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".