Bibliographic record
Abstract
hen we first proposed this issue of Intermédialités/Intermediality in Fall 2018, the world was gripped with impending changes and challenges to border regions.The United Kingdom was grappling with the long-term implications of the 2016 Brexit vote.Mass migration continued to flow between North African and Middle Eastern nations and Europe, placing new strains on the European Union amidst a tide of populist politics.In the United States, the Trump administration had turned the North American Free Trade Agreement upside down and was simultaneously demanding completion of a 3,145-kilometer physical wall along the US-Mexican border.The US-Mexico border became one of President Donald Trump's first and favoured targets.Leveraging the publicity around the migrant caravan through Central America and Mexico, Trump renewed an ongoing campaign to further fortify an already securitized landscape in latching onto the symbolic prospect of fortress America.This blustery proposal served as a political football for much of 2018 while the Trump administration steadily and quietly increased the number of immigrant detainees indefinitely, separating children from their families.At the same time, new anti-immigrant policies sent shockwaves through American immigrant communities as bans on immigration and related measures led to a temporary refugee crisis on America's other (northern) border.As US visas expired for Syrians, Somalis, Haitians, and others, many traveled north to claim refugee status at rural, makeshift locations on the Canada-US border in a series of surreal scenes at Roxham Road on the Quebec-New York border as well as in the small community of Emmerson, Manitoba.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.155 | 0.068 |
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".