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Record W7039733651

Never pass a mirror without looking in it

2019· dissertation· en· W7039733651 on OpenAlexaffabout

Bibliographic record

VenueMspace (University of Manitoba) · 2019
Typedissertation
Languageen
FieldComputer Science
TopicInteractive and Immersive Displays
Canadian institutionsUniversity of WinnipegUniversity of Manitoba
Fundersnot available
KeywordsExhibitionFace (sociological concept)Order (exchange)State (computer science)Photography
DOInot available

Abstract

fetched live from OpenAlex

Our bodies face continuous stress throughout a lifetime, physically and mentally deteriorating and changing as we age. As humans, we each have a unique vessel and I am in a constant state of wishing to preserve my own. Never Pass a Mirror Without Looking in It is an exhibition consisting of manipulated Polaroid images. There are nine floor-based installation works that are meant to be walked through and around. Viewers are urged to strain their bodies in order to search for a figure within each piece. Breanne grew up in Winnipeg Manitoba where she spent the majority of her life as part of the Manta Swim club. This seventeen-year career, that included representing Canada in swimming competitions all over the world, became a huge influence on her artistic practice.

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)0.000
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.038
Threshold uncertainty score0.129

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0000.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0380.020

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 head0.014
GPT teacher head0.233
Teacher spread0.219 · 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 designNot applicable
Domainnot available
GenreOther

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 routes2
Has abstractyes

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