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Record W4240220560 · doi:10.31542/j.muse.207

Flatland

2015· article· en· W4240220560 on OpenAlexaffvenue
Casey Shea Pollon, Christine M. Carey, Travis Mason Champagne, Kaitlyn Michelle Dirk, Jonathan Andrew Dyck, David Thomas Hamel-Carnduff, Robyn Laurel Huizinga, Shelby Rae Johnson, Roisin Maire Danaan McElhatton, Shanelle Lee Pender, Gerhard Peter Penner, Chunyu Qi, Jordan Anton Rude, Jessica Kristine Schmidt, Paulina Dawn Smit, Andrew Thomas Wedman, Morgan Donald Wellborn, Renee Marie Wood, Amy Nicole Yoder, Marina Eastwood

Bibliographic record

VenueMacEwan University Student eJournal · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicVisual Culture and Art Theory
Canadian institutionsMacEwan University
Fundersnot available
KeywordsTypographyNovellaComputer scienceRomanceSection (typography)Desktop publishingArtVisual artsLiterature

Abstract

fetched live from OpenAlex

Flatland is a project of VCDE233 TYPOGRAPHY II and VCDI223 DESIGN AND PRE-PRESS PRODUCTION, both courses in the Design Studies diploma program at MacEwan University. Students were asked to translate an assigned section of the Victorian novella, Flatland: A Romance of Many Dimensions by Edwin A. Abbott (1884), into a two-page layout that treats the text in a way that is visually appealing, readable, and appropriate to the content. They were encouraged to challenge conventions by exploring alternative grids, objective and expressive type, and text and image relationships. VCDE233 Typography II (Constanza Pacher) and VCDI223 Design and Pre-Press Production (Jess Dupuis)

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.317
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0030.001
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.3170.065

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 head0.051
GPT teacher head0.248
Teacher spread0.197 · 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
Published2015
Admission routes2
Has abstractyes

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