Bibliographic record
Abstract
Flight to Canada, and: Elevator Amina Gautier (bio) Flight to Canada The morning after the election, they awaken feeling drunk and dizzy, hoping either it was all a dream, or that, while they'd slept, those last red states would turn blue. They had been poised for a history making moment—the first black president being succeeded by the first woman—but instead they woke to a morning of mourning, a day of disappointment. They can't imagine staying here—they are afraid of what their country will become. The nicest thing they can say about their incoming leader is that he is inept, unqualified. But that they will not say because they will say nothing about him, will not mention his name within the sanctity of their home. They predict his win will give license to racial hatred; they fear what this election will pull from the shadows. "We're moving to Canada!" they announce before their morning coffee and even after the caffeine kicks in, their minds remain unchanged. There were rallying cries of moving to Canada back when Kerry lost, but they were grad students then and couldn't afford to become expatriates without finishing their degrees. This time around, they are working adults with salaries and emergency savings, although they haven't saved for an emergency like this. They have never been to Canada; the northern portion of the continent has never held their interest. They think of it only now that their backs are against the wall, now that they are cornered, now that the votes have betrayed their hopes, now that there will be a despot-in-chief, now that they want out. They pull boxes of files and go through all of their policies. They trudge out their calculators and sit at their kitchen table, barely able to see one another over the boxes piled high. What is the penalty for cashing out a 403b early? Which IRA is the one that dings you—the Roth or the not-Roth? How much money can they get their hands on without first having to retire? Frequent flyers miles! How many do they need for two one way tickets to Canada? But the numbers are depressing. Even the calculators are depressing—they never tally the numbers to match their hopes. "Which part do we want?" she asks. "That might make a difference in expense. The side above Washington or the New York/Vermont side? But we don't speak French." "That's only Montreal," he reminds her. "How about a city with a basketball team?" she suggests, knowing what he likes. "Vancouver? Toronto?" "Only Toronto," he says. "The Grizzlies went to Memphis." "You mean there's only one team in Canada and we still hang their flag in all our stadiums and sing the anthem?" She doesn't think this is fair. "Naismith was Canadian," he says. "It won't bother you once we're there." "Once we're there," she repeats, her breath catching on the promise. "Never been to a Raptors game," he says, wistful. He remembers the '98 draft, when Toronto traded [End Page 72] with Golden State for Vince Carter. The team was in its infancy back then, just three years old. He remembers Carter's first years—who could forget those amazing dunks? The fans had been crazy for him, their obsession dubbed "Vinsanity." The Raptors had good runs under Carter, then McGrady, and later under Bosh. Some of their former players and coaches were now Hall of Famers. "They've done well for a young team," he says. He wouldn't mind going to the games. She speculates, "Once we're there, we'll have to eat mayonnaise on our burgers and end our sentences with an uptick." She suggests a practice run to McDonald's. They bring back value meals, unwrap their burgers, peel back the top buns, and slather on mayonnaise. "After you," he says. "You first," she says, ever gracious. "Together," they say. They close their eyes and take tentative bites. The food sticks in their throats. "This tastes awful, eh?" he asks. They drink cupfuls of Canada Dry ginger ale to wash it down. The...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".