Bibliographic record
Abstract
In December 2013, Lucia Jimenez was caught paying less than the full fare for a public transit ticket. An undocumented Mexican national, Jimenez was taken into custody by the Canadian Border Services Agency. She hanged herself shortly thereafter. Following Jimenez’s death, a friend argued, “Lucia ended up being a ghost here.” Like so many non-status migrants for whom banal daily rituals—like accessing public transit—are dangerous, Jimenez practiced a necessary invisibility. But it wasn’t until her undocumented status came to light that she really disappeared: she entered the state’s “apparatus of disappearance, and vanished in plain sight” (Nield). Given the technologies of surveillance at work in detention facilities, it seems counterintuitive to constitute them as places where one can vanish, but such is the case in Canada, where there is no upper limit on the length of immigration detention. Tings Chak takes up these issues in her 2015 graphic essay, Undocumented: the Architecture of Migrant Detention, arguing that there is a pressing need “to make visible the sites and stories of detention.” With attention to Chak’s book and to the circumstances surrounding Jimenez’s death, this essay takes up the call to instigate a public conversation about immigration detention in Canada.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.042 | 0.014 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".