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Record W7059836334

Missed Fit

2019· dissertation· en· W7059836334 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueOCAD University Open Research Repository (OCAD University) · 2019
Typedissertation
Languageen
FieldEngineering
TopicMagnetic Field Sensors Techniques
Canadian institutionsOntario College of Art and Design
Fundersnot available
KeywordsMeaning (existential)Ideal (ethics)AnthropometryAffect (linguistics)SemioticsHuman body
DOInot available

Abstract

fetched live from OpenAlex

This thesis examines how the technical fit of a garment can affect an individual’s ability to fit in. It challenges the tool box used by practitioners working with anthropometric data (the surface measurements of the human body) and has produced new methods that are less reliant on published averages. Some of the thesis questions are: how does anthropometric data and the study of human anatomy influence notions of an ideal body? In what ways do anthropometric data and patternmaking principles include or exclude diverse body types? What tools can be developed to assist designing for diverse bodies? It takes a multi-method and multi-theory approach to the research and investigates concepts of fit through phenomenology, semiotics and anatomy. By exploring experimental methods in cut, it challenges the meaning of a key example of conservatism and uniformity in tailoring, the grey flannel suit.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.952
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0030.000
Research integrity0.0010.002
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 head0.053
GPT teacher head0.287
Teacher spread0.233 · 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