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Record W4251685568 · doi:10.32920/ryerson.14657211.v1

Choreographing space: restoring human movement in workplace typologies

2021· preprint· en· W4251685568 on OpenAlexaff
Lauren Boyer

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicArchitecture and Computational Design
Canadian institutionsToronto Metropolitan UniversityAdvantage Forensics (Canada)
Fundersnot available
KeywordsHuman bodyProduct (mathematics)Space (punctuation)Order (exchange)Movement (music)Environmental ethicsSociologyAestheticsBusinessMedicineComputer scienceArt

Abstract

fetched live from OpenAlex

The mechanics of the human body are becoming increasingly static. In the past, our economic milieu relied on human energy, but in today’s workplace, we are predominantly inactive. We have engineered human activity out of our physical environments and created a dependence on mechanisms to move us. Consequently, these rapid changes in the environments we inhabit have resulted in a rapid increase in chronic diseases due to inactivity. Growing evidence suggests that today’s chronic illnesses are a product of our modern lifestyle, and our lifestyle is a product of the spatial environments we inhabit. With this in mind, our spatial environment can be the cause and the solution to our sedentary modern condition - by radicalizing its shape it can re-shape the lives of its inhabitants. This thesis examines how human movement can be choreographed into the spatial design of the contemporary workplace environment in order to facilitate healthy lifestyles and sustainable future societies.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0050.006
Open science0.0010.007
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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
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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