Bibliographic record
Abstract
Harsha Walia, Undoing Border Imperialism (Oakland, CA: AK Press, 2013), 321 pages, $16.00, paperback.One December day in 2007, two thousand people showed up at Vancouver's International Airport. Unlike other days, these particular people had not come to catch a flight; they were there to stop a person from boarding one. Laibar Singh, a paralyzed refugee from India, was facing deportation. On the day he was to leave, those two thousand people, mostly Punjabi elders and aunties, shut down the international terminal, causing the cancellation of dozens of flights. They formed a protective circle around Singh for hours, finally forcing immigration enforcement to back down.… "This historic blockade in December 2007 is the only documented time in recent North American history that the violence of deportation has been prevented through the power of a mass mobilization and direct action," wrote Harsha Walia, one of the organizers responsible for this mass mobilization and the author and editor of Undoing Border Imperialism. This article can also be found at the Monthly Review website, where most recent articles are published in full.Click here to purchase a PDF version of this article at the Monthly Review website.
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 | 0.090 | 0.192 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.007 | 0.023 |
| Scholarly communication | 0.029 | 0.033 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.018 | 0.005 |
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".