MétaCan
Menu
Back to cohort
Record W7001950530

Managing Cultural Specificity and Universality in Trey Anthony’s Da Kink in My Hair and Ins Choi’s Kim’s Convenience

2024· other· en· W7001950530 on OpenAlexaboutno aff

Bibliographic record

VenueYork University Digital Library (York University) · 2024
Typeother
Languageen
FieldComputer Science
TopicNonlinear Dynamics and Pattern Formation
Canadian institutionsnot available
Fundersnot available
KeywordsNOMINATEUniversality (dynamical systems)Set (abstract data type)Editorial board
DOInot available

Abstract

fetched live from OpenAlex

This essay won the Department of English's Avie Bennett Prize in Canadian Literature in 2024. Faculty members nominate students for this award. Avie Bennett was Chancellor of York University from May 1998 to June 2004. Because of his lifelong interest in Canadian Studies, he has established a prize to be awarded annually to an undergraduate student studying in the Department of English in the Faculty of LA&PS, who has written the best essay in Canadian Literature. The recipient will also receive a set of the New Canadian Library at the annual English Department awards reception.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.020
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.012
GPT teacher head0.176
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueYork University Digital Library (York University)Same topicNonlinear Dynamics and Pattern FormationFrench-language works237,207