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Record W3216418319 · doi:10.32920/ryerson.14645523.v1

On Slowness And Inhabiting Time

2021· preprint· en· W3216418319 on OpenAlexaff
Sahel Tahvildari

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldArts and Humanities
TopicArt, Politics, and Modernism
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsSlownessEveryday lifeEmotiveAestheticsEmbodied cognitionParallelsArchitecturePsychologyDistractionPerceptionRhythmCognitive psychologySociologyArtVisual artsPhilosophyEngineeringEpistemology

Abstract

fetched live from OpenAlex

As a result of today’s accelerated culture, the omnipresence of speed has taken over everyday life. The influence of flexibility and efficiency within contemporary architecture parallels the need for speed. The primacy of speed has hindered the temporal and cognitive presence in everyday life; shifting the human experience of the world from lived to measured. Through the reconsideration of slowness as an embodied experience, the rhythmic passage of the body through space establishes a mindful physical presence as opposed to modern distraction. In architecture, spatial and perceptual unfolding intertwines perceiver and perceived to sequentially reveal the layers of experience through passage. Within slowness, visual fluidity creates a viscous and emotive relationship between mind, body, form, and movement. The relationship between speed, time, and presence, guides the mindful body through a layered procession which unfolds in the creation of an architecture of slowness.

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)0.004
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: Other · Consensus signal: Other
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.021
Scholarly communication0.0060.008
Open science0.0000.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.044
GPT teacher head0.243
Teacher spread0.200 · 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
Published2021
Admission routes1
Has abstractyes

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