On the Poetics of Migration, Black Geographies, and Nervous Conditions
Bibliographic record
Abstract
This conversation began in Winter 2023, at Queen’s University in Kingston, Ontario, Canada. At the time we were collectively studying Black geographies. As faculty and students who are part of the Black diaspora yet have very different experiences of displacement, our discussions revolved around the uneasy connections between education, university life, migration, and Blackness. Inspired by the writings of Simone Browne, on January 25, 2024, the Revolutionary Demand for Happiness working group organized a conversation that revolved around the poetic possibilities of migration, mobility, immobility, borders, and boundaries. Paul Akpomuje and Aaliyah Strachan organized the event, and it was moderated by Katherine McKittrick. Akpomuje, a Nigerian poet and doctoral student in the Queen’s University Faculty of Education, read a set of his poems that discussed themes such as surveillance, visas, travel, and home. The poems were paired with community stories about displacement and belonging; we made connections between over-policing, governmentality and government papers, racial capitalism, family and kinship ties, and the difficult and onerous work of traveling as members of the Black diaspora. We also homed in on understanding the racialized underpinnings of the “international student” category at Queen’s University—a figure that is monetarily required-desired, yet is also rendered institutionally unrooted and out of 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.029 | 0.044 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| 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".