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Record W2955043852 · doi:10.1353/plc.2019.0112

Flight to Canada, and: Elevator

2019· article· en· W2955043852 on OpenAlexaboutno aff
Amina Gautier

Bibliographic record

VenuePleiades · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsnot available
Fundersnot available
KeywordsNothingDreamHatredDisappointmentParadeFeelingWhite (mutation)HistoryPolitical scienceLawMedia studiesSociologyPsychologyPoliticsPhilosophySocial psychology

Abstract

fetched live from OpenAlex

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...

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.004
GPT teacher head0.194
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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