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Record W4247569389 · doi:10.32920/ryerson.14644959

Mozaiq: An Inclusive Tool for Curating and Personalizing 2D Stock Animation for People of all Ethnic Backgrounds

2021· preprint· en· W4247569389 on OpenAlexaff
Ebyan Bihi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicHuman Motion and Animation
Canadian institutionsConcordia UniversityToronto Metropolitan University
Fundersnot available
KeywordsAnimationStorytellingDiversity (politics)Ethnic groupInclusion (mineral)Bridge (graph theory)Stock (firearms)Computer scienceSociologyGeographyNarrativeComputer graphics (images)ArtSocial scienceLiteratureAnthropologyArchaeology

Abstract

fetched live from OpenAlex

When it comes to diversity, and inclusion, what does the current 2D animation landscape look like? This paper offers an in-depth review, and critique of the two most commonly used animation software platforms, Powtoon and Animaker. It highlights the diversity gaps found within these platforms, whilst proposing a platform (Mozaiq) that could potentially bridge these gaps, and provide more inclusive storytelling for all.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.787
Threshold uncertainty score0.876

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)0.0000.000

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 head0.061
GPT teacher head0.331
Teacher spread0.270 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2021
Admission routes1
Has abstractyes

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