Site Fight! Toward the Abolition of Immigrant Detention on Tacoma’s Tar Pits (and Everywhere Else)
Bibliographic record
Abstract
Abstract This paper theorises the spatialisation of White supremacy through the siting and expansion of a US immigrant detention centre, the Northwest Detention Center (NWDC). This case reveals the spatial relationship between the detention centre’s displacement with the Seattle‐Tacoma region’s increasing wealth, highlighting the role of detention and incarceration in the spatialisation of White supremacy. If White advantage maps onto whiteness as property, then White supremacy maps onto interlocking systems of settler colonialism and racial capitalism that dispossess people of colour of land and turns their bodies into devalued pollution sinks, where the less‐than‐citizen is forced to live on Tar Pits that they cannot even call “home”. Since 2014, detained immigrants’ activism has fuelled conversations about the punitive nature of administrative immigrant detention, racial profiling, and the city’s responsibility to enforce health, safety and environmental regulations for all residents. Through the stories of detainees, deportees and their co‐conspirators, this site fight illustrates how abolition ecologies call for tearing down toxic detention centres. Beyond rejecting White supremacist logics in immigration enforcement, abolitionists make freedom as a place together.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.015 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".