Homeland (In)Security : Roots and Displacement, from New York, to Toronto, to Salt Lake City
Bibliographic record
Abstract
[First paragraph] On September 11, 2001, broadcast and print media around the world narrated the destruction of New York City's World Trade Center and the deaths of thousands of its occupants. This paper will examine the ways in which television and its appendages (the telephone, the internet, the newspaper) operated to organize the discursive meanings of this traumatic event. I will propose that between September 11 and February 2002's Winter Olympics in Salt Lake City, Utah, ideas about home, roots and rootedness (and their Others: foreignness, homelessness, nomadicism) operated as a discursive mapping of that which could otherwise not be mapped, or fully grasped. From the demonization of those nomads, migrants and others moving across borders, to the triumphant Roots logos on the uniforms of British, American and Canadian Olympic athletes, roots, in a sense, became the unrepresentable real. How did television, then, in its liminal position on the borders of the home, narratively organize the spatial boundaries of inside and outside, local and global? More specifically, I want to examine how Canadian television worked to mediate the trauma of boundary dissolution in both a literal and a representational sense. Following along recent trauma theory, I want to ask: how does a nation itself experiences common symptoms of trauma? And can these fears become unrepresentable in and of themselves, so that the metaphor of home and roots stand in their place?
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".